{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "411b8672",
   "metadata": {},
   "source": [
    "# 在多分类任务实验中实现momentum、rmsprop、adam优化器"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d4ca751",
   "metadata": {},
   "source": [
    "## 手动实现momentum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b6073b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前使用的device为cuda\n"
     ]
    }
   ],
   "source": [
    "# 导入模块\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "import time\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # 如果有gpu则在gpu上计算 加快计算速度\n",
    "print(f'当前使用的device为{device}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9a08672c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义绘图函数\n",
    "import matplotlib.pyplot as plt\n",
    "def draw(name,data,xlabel,ylabel):\n",
    "    plt.figure(figsize=(8, 3))\n",
    "    plt.title(name[-1])\n",
    "    for i in range(len(name)-1):\n",
    "        plt.plot(data[i],label=name[i])\n",
    "        plt.xlabel(xlabel)\n",
    "        plt.ylabel(ylabel)\n",
    "        plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60d8b1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 多分类任务\n",
    "traindataset = torchvision.datasets.FashionMNIST(root='./FashionMNIST', train=True, download=True, transform=transforms.ToTensor())\n",
    "testdataset = torchvision.datasets.FashionMNIST(root='./FashionMNIST', train=False, download=True, transform=transforms.ToTensor())\n",
    "batch_size = 256\n",
    "traindataloader = torch.utils.data.DataLoader(traindataset, batch_size=batch_size, shuffle=True, num_workers=0)\n",
    "testdataloader = torch.utils.data.DataLoader(testdataset, batch_size=batch_size, shuffle=False, num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c8617e4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义自己的前馈神经网络\n",
    "class Net():\n",
    "    def __init__(self):\n",
    "        # 设置隐藏层和输出层的节点数\n",
    "        num_inputs, num_hiddens, num_outputs = 28 * 28, 256, 10  # 十分类问题\n",
    "        w_1 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_inputs)), dtype=torch.float32)\n",
    "        b_1 = torch.zeros(num_hiddens, dtype=torch.float32)\n",
    "        w_2 = torch.tensor(np.random.normal(0, 0.01, (num_outputs, num_hiddens)), dtype=torch.float32)\n",
    "        b_2 = torch.zeros(num_outputs, dtype=torch.float32)\n",
    "        self.params = [w_1, b_1, w_2, b_2]\n",
    "        self.w=[w_1,w_2]\n",
    "        for param in self.params:\n",
    "            param.requires_grad = True\n",
    "        # 定义模型结构\n",
    "        self.input_layer = lambda x: x.view(x.shape[0], -1)\n",
    "        self.hidden_layer = lambda x: self.my_relu(torch.matmul(x, w_1.t()) + b_1)\n",
    "        self.output_layer = lambda x: torch.matmul(x, w_2.t()) + b_2\n",
    "\n",
    "    def my_relu(self, x):\n",
    "        return torch.max(input=x, other=torch.tensor(0.0))\n",
    "\n",
    "    # 定义前向传播\n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.hidden_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "def mySGD(params, lr, batchsize):\n",
    "    for param in params:\n",
    "        param.data -= lr * param.grad / batchsize\n",
    "\n",
    "# 定义损失函数\n",
    "def my_cross_entropy_loss(y_hat, labels):\n",
    "    def log_softmax(y_hat):\n",
    "        max_v = torch.max(y_hat, dim=1).values.unsqueeze(dim=1)\n",
    "        return y_hat - max_v - torch.log(torch.exp(y_hat-max_v).sum(dim=1).unsqueeze(dim=1))\n",
    "\n",
    "    return (-log_softmax(y_hat))[range(len(y_hat)), labels].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6bd44ed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 手动实现momentum算法\n",
    "def init_momentum_states(params):\n",
    "    v_w1,v_b1,v_w2,v_b2=torch.zeros(params[0].shape),torch.zeros(params[1].shape),torch.zeros(params[2].shape),torch.zeros(params[3].shape)\n",
    "    return (v_w1,v_b1,v_w2,v_b2)\n",
    "def sgd_momentum(params,states,lr=0.01,momentum=0.5):\n",
    "    for p,v in zip(params,states):\n",
    "        with torch.no_grad():\n",
    "            v[:]=momentum*v-p.grad\n",
    "            p[:]+=lr*v\n",
    "        p.grad.data.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4c56ed7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(net,train_iter,test_iter,loss_func,epochs,lr,optimizer,init_states=None):\n",
    "    train_loss_list=[]\n",
    "    train_acc_list=[]\n",
    "    test_loss_list=[]\n",
    "    test_acc_list=[]\n",
    "    states=init_states(net.params)\n",
    "    begin_time=time.time()\n",
    "    for epoch in range(epochs):\n",
    "        train_loss_sum,train_acc_sum,n1,c=0.0,0.0,0,0\n",
    "        for X,y in train_iter:\n",
    "            y_hat=net.forward(X)\n",
    "            l=loss_func(y_hat,y).sum()\n",
    "            l.backward()\n",
    "            optimizer(net.params,states,lr)\n",
    "            for param in net.params:\n",
    "                param.grad.data.zero_()\n",
    "            train_loss_sum+=l.item()\n",
    "            train_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()\n",
    "            n1+=y.shape[0]\n",
    "            c+=1\n",
    "        train_loss_list.append(train_loss_sum/n1)\n",
    "        train_acc_list.append(train_acc_sum/len(traindataset))\n",
    "        with torch.no_grad():\n",
    "            test_loss_sum,test_acc_sum,n2,c=0.0,0.0,0,0\n",
    "            for X,y in test_iter:\n",
    "                y_hat=net.forward(X)\n",
    "                l=loss_func(y_hat,y).sum()\n",
    "                test_loss_sum+=l.item()\n",
    "                test_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()\n",
    "                n2+=y.shape[0]\n",
    "                c+=1\n",
    "            test_loss_list.append(test_loss_sum/n2)\n",
    "            test_acc_list.append(test_acc_sum/len(testdataset))\n",
    "        print('epoch: %d | train loss:%.4f | test loss:%.4f | train acc: %.4f | test acc: %.4f'\n",
    "              % (epoch + 1, train_loss_sum/n1, test_loss_sum/n2, train_acc_sum/len(traindataset),test_acc_sum/len(testdataset)))\n",
    "    end_time=time.time()\n",
    "    print(\"%d轮 总用时: %.2fs\" % (epochs, end_time - begin_time))\n",
    "    return train_loss_list,test_loss_list,train_acc_list,test_acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "38dd9470",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0072 | test loss:0.0051 | train acc: 0.4832 | test acc: 0.6166\n",
      "epoch: 2 | train loss:0.0040 | test loss:0.0035 | train acc: 0.6554 | test acc: 0.6766\n",
      "epoch: 3 | train loss:0.0032 | test loss:0.0031 | train acc: 0.7006 | test acc: 0.7000\n",
      "epoch: 4 | train loss:0.0028 | test loss:0.0028 | train acc: 0.7408 | test acc: 0.7465\n",
      "epoch: 5 | train loss:0.0026 | test loss:0.0026 | train acc: 0.7683 | test acc: 0.7690\n",
      "epoch: 6 | train loss:0.0024 | test loss:0.0025 | train acc: 0.7873 | test acc: 0.7796\n",
      "epoch: 7 | train loss:0.0023 | test loss:0.0024 | train acc: 0.8011 | test acc: 0.7910\n",
      "epoch: 8 | train loss:0.0022 | test loss:0.0023 | train acc: 0.8108 | test acc: 0.7985\n",
      "epoch: 9 | train loss:0.0021 | test loss:0.0022 | train acc: 0.8183 | test acc: 0.8049\n",
      "epoch: 10 | train loss:0.0020 | test loss:0.0021 | train acc: 0.8231 | test acc: 0.8126\n",
      "10轮 总用时: 96.00s\n"
     ]
    }
   ],
   "source": [
    "net11=Net()\n",
    "momentum=0.5\n",
    "trainloss11,testloss11,trainacc11,testacc11=train(net11,traindataloader,testdataloader,my_cross_entropy_loss,10,0.01,sgd_momentum,init_momentum_states)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "592f4856",
   "metadata": {},
   "source": [
    "## torch.nn实现momentum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e29c09a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NetNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NetNN,self).__init__()\n",
    "        num_inputs,num_hiddens,num_outputs=784,256,10\n",
    "        self.input_layer=nn.Flatten()\n",
    "        self.hidden_layer=nn.Linear(num_inputs,num_hiddens)\n",
    "        self.output_layer=nn.Linear(num_hiddens,num_outputs)\n",
    "        self.relu=nn.ReLU()\n",
    "    def forward(self,x):\n",
    "        x=self.input_layer(x)\n",
    "        x=self.relu(self.hidden_layer(x))\n",
    "        x=self.output_layer(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7c934627",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainNN(net,train_iter,test_iter,loss_func,epochs,lr,optimizer):\n",
    "    train_loss_list=[]\n",
    "    train_acc_list=[]\n",
    "    test_loss_list=[]\n",
    "    test_acc_list=[]\n",
    "    begin_time=time.time()\n",
    "    for epoch in range(epochs):\n",
    "        train_loss_sum,train_acc_sum,n1,c=0.0,0.0,0,0\n",
    "        for X,y in train_iter:\n",
    "            y_hat=net.forward(X)\n",
    "            l=loss_func(y_hat,y)\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            train_loss_sum+=l.item()\n",
    "            train_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()\n",
    "            n1+=y.shape[0]\n",
    "            c+=1\n",
    "        train_loss_list.append(train_loss_sum/n1)\n",
    "        train_acc_list.append(train_acc_sum/len(traindataset))\n",
    "        with torch.no_grad():\n",
    "            test_loss_sum,test_acc_sum,n2,c=0.0,0.0,0,0\n",
    "            for X,y in test_iter:\n",
    "                y_hat=net.forward(X)\n",
    "                l=loss_func(y_hat,y).sum()\n",
    "                test_loss_sum+=l.item()\n",
    "                test_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()\n",
    "                n2+=y.shape[0]\n",
    "                c+=1\n",
    "            test_loss_list.append(test_loss_sum/n2)\n",
    "            test_acc_list.append(test_acc_sum/len(testdataset))\n",
    "        print('epoch: %d | train loss:%.4f | test loss:%.4f | train acc: %.4f | test acc: %.4f'\n",
    "              % (epoch + 1, train_loss_sum/n1, test_loss_sum/n2, train_acc_sum/len(traindataset),test_acc_sum/len(testdataset)))\n",
    "    end_time=time.time()\n",
    "    print(\"%d轮 总用时: %.2fs\" % (epochs, end_time - begin_time))\n",
    "    return train_loss_list,test_loss_list,train_acc_list,test_acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d2d1e848",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0056 | test loss:0.0038 | train acc: 0.5928 | test acc: 0.6775\n",
      "epoch: 2 | train loss:0.0032 | test loss:0.0030 | train acc: 0.7253 | test acc: 0.7366\n",
      "epoch: 3 | train loss:0.0027 | test loss:0.0027 | train acc: 0.7691 | test acc: 0.7681\n",
      "epoch: 4 | train loss:0.0024 | test loss:0.0025 | train acc: 0.7927 | test acc: 0.7877\n",
      "epoch: 5 | train loss:0.0023 | test loss:0.0023 | train acc: 0.8073 | test acc: 0.8012\n",
      "epoch: 6 | train loss:0.0022 | test loss:0.0022 | train acc: 0.8165 | test acc: 0.8085\n",
      "epoch: 7 | train loss:0.0021 | test loss:0.0021 | train acc: 0.8229 | test acc: 0.8145\n",
      "epoch: 8 | train loss:0.0020 | test loss:0.0021 | train acc: 0.8277 | test acc: 0.8163\n",
      "epoch: 9 | train loss:0.0019 | test loss:0.0021 | train acc: 0.8320 | test acc: 0.8180\n",
      "epoch: 10 | train loss:0.0019 | test loss:0.0020 | train acc: 0.8340 | test acc: 0.8234\n",
      "10轮 总用时: 94.89s\n"
     ]
    }
   ],
   "source": [
    "net12=NetNN()\n",
    "momentum=0.5\n",
    "momentum_optimizer=torch.optim.SGD(net12.parameters(),lr=0.01,momentum=0.5)\n",
    "trainloss12,testloss12,trainacc12,testacc12=trainNN(net12,traindataloader,testdataloader,torch.nn.CrossEntropyLoss(),10,0.01,momentum_optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4da840f",
   "metadata": {},
   "source": [
    "## 手动实现rmsprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c0191f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_rmsprop_states(params):\n",
    "    s_w1,s_b1,s_w2,s_b2=torch.zeros(params[0].shape),torch.zeros(params[1].shape),torch.zeros(params[2].shape),torch.zeros(params[3].shape)\n",
    "    return (s_w1,s_b1,s_w2,s_b2)\n",
    "def rmsprop(params,states,lr=0.01,gamma=0.9):\n",
    "    gamma,eps=gamma,1e-6\n",
    "    for p,s in zip(params,states):\n",
    "        with torch.no_grad():\n",
    "            s[:]=gamma*s+(1-gamma)*torch.square(p.grad)\n",
    "            p[:]-=lr*p.grad/torch.sqrt(s+eps)\n",
    "        p.grad.data.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9ac9b837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0028 | test loss:0.0024 | train acc: 0.7409 | test acc: 0.7687\n",
      "epoch: 2 | train loss:0.0018 | test loss:0.0025 | train acc: 0.8264 | test acc: 0.7814\n",
      "epoch: 3 | train loss:0.0017 | test loss:0.0021 | train acc: 0.8435 | test acc: 0.8116\n",
      "epoch: 4 | train loss:0.0016 | test loss:0.0023 | train acc: 0.8542 | test acc: 0.7938\n",
      "epoch: 5 | train loss:0.0015 | test loss:0.0029 | train acc: 0.8605 | test acc: 0.7385\n",
      "epoch: 6 | train loss:0.0014 | test loss:0.0017 | train acc: 0.8649 | test acc: 0.8494\n",
      "epoch: 7 | train loss:0.0014 | test loss:0.0017 | train acc: 0.8702 | test acc: 0.8528\n",
      "epoch: 8 | train loss:0.0013 | test loss:0.0017 | train acc: 0.8727 | test acc: 0.8555\n",
      "epoch: 9 | train loss:0.0013 | test loss:0.0019 | train acc: 0.8768 | test acc: 0.8362\n",
      "epoch: 10 | train loss:0.0013 | test loss:0.0018 | train acc: 0.8795 | test acc: 0.8404\n",
      "10轮 总用时: 93.11s\n"
     ]
    }
   ],
   "source": [
    "net21=Net()\n",
    "trainloss21,testloss21,trainacc21,testacc21=train(net21,traindataloader,testdataloader,my_cross_entropy_loss,10,0.01,rmsprop,init_rmsprop_states)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "794c9153",
   "metadata": {},
   "source": [
    "## torch.nn实现rmsprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ccad9703",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0036 | test loss:0.0028 | train acc: 0.7385 | test acc: 0.7462\n",
      "epoch: 2 | train loss:0.0018 | test loss:0.0020 | train acc: 0.8299 | test acc: 0.8304\n",
      "epoch: 3 | train loss:0.0017 | test loss:0.0027 | train acc: 0.8472 | test acc: 0.7763\n",
      "epoch: 4 | train loss:0.0015 | test loss:0.0017 | train acc: 0.8601 | test acc: 0.8411\n",
      "epoch: 5 | train loss:0.0015 | test loss:0.0019 | train acc: 0.8662 | test acc: 0.8496\n",
      "epoch: 6 | train loss:0.0014 | test loss:0.0017 | train acc: 0.8719 | test acc: 0.8560\n",
      "epoch: 7 | train loss:0.0013 | test loss:0.0020 | train acc: 0.8762 | test acc: 0.8476\n",
      "epoch: 8 | train loss:0.0013 | test loss:0.0016 | train acc: 0.8801 | test acc: 0.8675\n",
      "epoch: 9 | train loss:0.0013 | test loss:0.0018 | train acc: 0.8842 | test acc: 0.8588\n",
      "epoch: 10 | train loss:0.0012 | test loss:0.0018 | train acc: 0.8849 | test acc: 0.8626\n",
      "10轮 总用时: 95.20s\n"
     ]
    }
   ],
   "source": [
    "net22=NetNN()\n",
    "rmsprop_optimizer=torch.optim.RMSprop(net22.parameters(),lr=0.01,alpha=0.9,eps=1e-6)\n",
    "trainloss22,testloss22,trainacc22,testacc22=trainNN(net22,traindataloader,testdataloader,torch.nn.CrossEntropyLoss(),10,0.01,rmsprop_optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57539871",
   "metadata": {},
   "source": [
    "## 手动实现adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d11b36e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_adam_states(params):\n",
    "    v_w1,v_b1,v_w2,v_b2=torch.zeros(params[0].shape),torch.zeros(params[1].shape),torch.zeros(params[2].shape),torch.zeros(params[3].shape)\n",
    "    s_w1,s_b1,s_w2,s_b2=torch.zeros(params[0].shape),torch.zeros(params[1].shape),torch.zeros(params[2].shape),torch.zeros(params[3].shape)\n",
    "    return ((v_w1,s_w1),(v_b1,s_b1),(v_w2,s_w2),(v_b2,s_b2))\n",
    "def adam(params,states,lr=0.01,t=1):\n",
    "    beta1,beta2,eps=0.9,0.999,1e-6\n",
    "    for p,(v,s) in zip(params,states):\n",
    "        with torch.no_grad():\n",
    "            v[:]=beta1*v+(1-beta1)*p.grad\n",
    "            s[:]=beta2*s+(1-beta2)*(p.grad**2)\n",
    "            v_=v/(1-beta1**t)\n",
    "            s_=s/(1-beta2**t)\n",
    "        p.data-=lr*v_/torch.sqrt(s_+eps)\n",
    "        p.grad.data.zero_()\n",
    "    t+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "15c044c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0020 | test loss:0.0016 | train acc: 0.8078 | test acc: 0.8515\n",
      "epoch: 2 | train loss:0.0014 | test loss:0.0015 | train acc: 0.8689 | test acc: 0.8645\n",
      "epoch: 3 | train loss:0.0013 | test loss:0.0014 | train acc: 0.8812 | test acc: 0.8729\n",
      "epoch: 4 | train loss:0.0012 | test loss:0.0015 | train acc: 0.8887 | test acc: 0.8683\n",
      "epoch: 5 | train loss:0.0011 | test loss:0.0014 | train acc: 0.8940 | test acc: 0.8756\n",
      "epoch: 6 | train loss:0.0011 | test loss:0.0013 | train acc: 0.8976 | test acc: 0.8768\n",
      "epoch: 7 | train loss:0.0010 | test loss:0.0013 | train acc: 0.9012 | test acc: 0.8808\n",
      "epoch: 8 | train loss:0.0010 | test loss:0.0013 | train acc: 0.9062 | test acc: 0.8800\n",
      "epoch: 9 | train loss:0.0010 | test loss:0.0013 | train acc: 0.9075 | test acc: 0.8842\n",
      "epoch: 10 | train loss:0.0009 | test loss:0.0013 | train acc: 0.9095 | test acc: 0.8796\n",
      "10轮 总用时: 97.69s\n"
     ]
    }
   ],
   "source": [
    "net31=Net()\n",
    "trainloss31,testloss31,trainacc31,testacc31=train(net31,traindataloader,testdataloader,my_cross_entropy_loss,10,0.01,adam,init_adam_states)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88611f9a",
   "metadata": {},
   "source": [
    "## torch.nn实现adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3c7fc098",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0021 | test loss:0.0017 | train acc: 0.8055 | test acc: 0.8484\n",
      "epoch: 2 | train loss:0.0015 | test loss:0.0016 | train acc: 0.8643 | test acc: 0.8534\n",
      "epoch: 3 | train loss:0.0013 | test loss:0.0020 | train acc: 0.8739 | test acc: 0.8152\n",
      "epoch: 4 | train loss:0.0013 | test loss:0.0017 | train acc: 0.8798 | test acc: 0.8447\n",
      "epoch: 5 | train loss:0.0012 | test loss:0.0016 | train acc: 0.8847 | test acc: 0.8597\n",
      "epoch: 6 | train loss:0.0012 | test loss:0.0015 | train acc: 0.8891 | test acc: 0.8612\n",
      "epoch: 7 | train loss:0.0011 | test loss:0.0014 | train acc: 0.8920 | test acc: 0.8750\n",
      "epoch: 8 | train loss:0.0011 | test loss:0.0015 | train acc: 0.8932 | test acc: 0.8680\n",
      "epoch: 9 | train loss:0.0011 | test loss:0.0015 | train acc: 0.8955 | test acc: 0.8746\n",
      "epoch: 10 | train loss:0.0011 | test loss:0.0015 | train acc: 0.8989 | test acc: 0.8700\n",
      "10轮 总用时: 99.95s\n"
     ]
    }
   ],
   "source": [
    "net32=NetNN()\n",
    "adam_optimizer=torch.optim.Adam(net32.parameters(),lr=0.01,betas=(0.9,0.999),eps=1e-6)\n",
    "trainloss32,testloss32,trainacc32,testacc32=trainNN(net32,traindataloader,testdataloader,torch.nn.CrossEntropyLoss(),10,0.01,adam_optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "deb5f95e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['momentum','RMSprop','Adam','Train Loss']\n",
    "train_loss=[trainloss12,trainloss22,trainloss32]\n",
    "draw(name,train_loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "83fb8b22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['momentum','RMSprop','Adam','Test Loss']\n",
    "test_loss=[testloss12,testloss22,testloss32]\n",
    "draw(name,test_loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7ddf48cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['momentum','RMSprop','Adam','Train Acc']\n",
    "train_acc=[trainacc12,trainacc22,trainacc32]\n",
    "draw(name,train_acc,'Epoch','Acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ed7301ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['momentum','RMSprop','Adam','Test Acc']\n",
    "test_acc=[testacc12,testacc22,testacc32]\n",
    "draw(name,test_acc,'Epoch','Acc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d9e6a77",
   "metadata": {},
   "source": [
    "## 可以看到Adam性能比RMSprop好，RMSprop性能比Adam好"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c7dcf5",
   "metadata": {},
   "source": [
    "# 在多分类任务实验中分别手动实现和用torch.nn实现𝑳𝟐正则化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73578200",
   "metadata": {},
   "source": [
    "## 手动实现L2正则化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7723f8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def l2_penalty(w):\n",
    "    penalty=0\n",
    "    for i in range(len(w)):\n",
    "        penalty+=(w[i]**2).sum()\n",
    "    return penalty/2/256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9f9a0b4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_l2(net,train_iter,test_iter,loss_func,epochs,lr,optimizer,init_states=None,l2=True,lam=3):\n",
    "    train_loss_list=[]\n",
    "    train_acc_list=[]\n",
    "    test_loss_list=[]\n",
    "    test_acc_list=[]\n",
    "    states=init_states(net.params)\n",
    "    begin_time=time.time()\n",
    "    for epoch in range(epochs):\n",
    "        train_loss_sum,train_acc_sum,n1,c=0.0,0.0,0,0\n",
    "        for X,y in train_iter:\n",
    "            y_hat=net.forward(X)\n",
    "            l=loss_func(y_hat,y)\n",
    "            if l2==True:\n",
    "                l+=lam*l2_penalty(net.w)\n",
    "            l.backward()\n",
    "            optimizer(net.params,states,lr)\n",
    "            for param in net.params:\n",
    "                param.grad.data.zero_()\n",
    "            train_loss_sum+=l.item()\n",
    "            train_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()\n",
    "            n1+=y.shape[0]\n",
    "            c+=1\n",
    "        train_loss_list.append(train_loss_sum/n1)\n",
    "        train_acc_list.append(train_acc_sum/len(traindataset))\n",
    "        with torch.no_grad():\n",
    "            test_loss_sum,test_acc_sum,n2,c=0.0,0.0,0,0\n",
    "            for X,y in test_iter:\n",
    "                y_hat=net.forward(X)\n",
    "                l=loss_func(y_hat,y).sum()\n",
    "                test_loss_sum+=l.item()\n",
    "                test_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()\n",
    "                n2+=y.shape[0]\n",
    "                c+=1\n",
    "            test_loss_list.append(test_loss_sum/n2)\n",
    "            test_acc_list.append(test_acc_sum/len(testdataset))\n",
    "        print('epoch: %d | train loss:%.4f | test loss:%.4f | train acc: %.4f | test acc: %.4f'\n",
    "              % (epoch + 1, train_loss_sum/n1, test_loss_sum/n2, train_acc_sum/len(traindataset),test_acc_sum/len(testdataset)))\n",
    "    end_time=time.time()\n",
    "    print(\"%d轮 总用时: %.2fs\" % (epochs, end_time - begin_time))\n",
    "    return train_loss_list,test_loss_list,train_acc_list,test_acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6bd0e047",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0078 | test loss:0.0052 | train acc: 0.4570 | test acc: 0.5990\n",
      "epoch: 2 | train loss:0.0047 | test loss:0.0036 | train acc: 0.6493 | test acc: 0.6649\n",
      "epoch: 3 | train loss:0.0039 | test loss:0.0032 | train acc: 0.6955 | test acc: 0.7073\n",
      "epoch: 4 | train loss:0.0036 | test loss:0.0029 | train acc: 0.7342 | test acc: 0.7379\n",
      "epoch: 5 | train loss:0.0034 | test loss:0.0027 | train acc: 0.7621 | test acc: 0.7623\n",
      "epoch: 6 | train loss:0.0033 | test loss:0.0026 | train acc: 0.7813 | test acc: 0.7761\n",
      "epoch: 7 | train loss:0.0032 | test loss:0.0025 | train acc: 0.7945 | test acc: 0.7881\n",
      "epoch: 8 | train loss:0.0031 | test loss:0.0024 | train acc: 0.8034 | test acc: 0.7964\n",
      "epoch: 9 | train loss:0.0030 | test loss:0.0024 | train acc: 0.8101 | test acc: 0.8001\n",
      "epoch: 10 | train loss:0.0030 | test loss:0.0023 | train acc: 0.8142 | test acc: 0.8007\n",
      "10轮 总用时: 96.10s\n"
     ]
    }
   ],
   "source": [
    "net211=Net()\n",
    "momentum=0.5\n",
    "trainloss211,testloss211,trainacc211,testacc211=train_l2(net211,traindataloader,testdataloader,my_cross_entropy_loss,10,0.01,sgd_momentum,init_momentum_states,True,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "4467477c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0076 | test loss:0.0052 | train acc: 0.4769 | test acc: 0.6126\n",
      "epoch: 2 | train loss:0.0045 | test loss:0.0036 | train acc: 0.6505 | test acc: 0.6626\n",
      "epoch: 3 | train loss:0.0037 | test loss:0.0031 | train acc: 0.6987 | test acc: 0.7008\n",
      "epoch: 4 | train loss:0.0034 | test loss:0.0029 | train acc: 0.7369 | test acc: 0.7449\n",
      "epoch: 5 | train loss:0.0032 | test loss:0.0027 | train acc: 0.7633 | test acc: 0.7601\n",
      "epoch: 6 | train loss:0.0030 | test loss:0.0026 | train acc: 0.7814 | test acc: 0.7787\n",
      "epoch: 7 | train loss:0.0029 | test loss:0.0024 | train acc: 0.7955 | test acc: 0.7882\n",
      "epoch: 8 | train loss:0.0028 | test loss:0.0024 | train acc: 0.8059 | test acc: 0.7969\n",
      "epoch: 9 | train loss:0.0028 | test loss:0.0023 | train acc: 0.8115 | test acc: 0.8013\n",
      "epoch: 10 | train loss:0.0027 | test loss:0.0023 | train acc: 0.8176 | test acc: 0.8055\n",
      "10轮 总用时: 95.95s\n"
     ]
    }
   ],
   "source": [
    "net221=Net()\n",
    "momentum=0.5\n",
    "trainloss221,testloss221,trainacc221,testacc221=train_l2(net221,traindataloader,testdataloader,my_cross_entropy_loss,10,0.01,sgd_momentum,init_momentum_states,True,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa50232e",
   "metadata": {},
   "source": [
    "## torch.nn实现L2正则化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4ff60a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainNN_l2(net,train_iter,test_iter,loss_func,epochs,lr,optimizer,l2=True,lam=0.01):\n",
    "    train_loss_list=[]\n",
    "    train_acc_list=[]\n",
    "    test_loss_list=[]\n",
    "    test_acc_list=[]\n",
    "    if l2==True:\n",
    "        optimizer=torch.optim.SGD(net.parameters(),lr=lr,weight_decay=lam)\n",
    "    begin_time=time.time()\n",
    "    for epoch in range(epochs):\n",
    "        train_loss_sum,train_acc_sum,n1,c=0.0,0.0,0,0\n",
    "        for X,y in train_iter:\n",
    "            y_hat=net.forward(X)\n",
    "            l=loss_func(y_hat,y)\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "            train_loss_sum+=l.item()\n",
    "            train_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()\n",
    "            n1+=y.shape[0]\n",
    "            c+=1\n",
    "        train_loss_list.append(train_loss_sum/n1)\n",
    "        train_acc_list.append(train_acc_sum/len(traindataset))\n",
    "        with torch.no_grad():\n",
    "            test_loss_sum,test_acc_sum,n2,c=0.0,0.0,0,0\n",
    "            for X,y in test_iter:\n",
    "                y_hat=net.forward(X)\n",
    "                l=loss_func(y_hat,y).sum()\n",
    "                test_loss_sum+=l.item()\n",
    "                test_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()\n",
    "                n2+=y.shape[0]\n",
    "                c+=1\n",
    "            test_loss_list.append(test_loss_sum/n2)\n",
    "            test_acc_list.append(test_acc_sum/len(testdataset))\n",
    "        print('epoch: %d | train loss:%.4f | test loss:%.4f | train acc: %.4f | test acc: %.4f'\n",
    "              % (epoch + 1, train_loss_sum/n1, test_loss_sum/n2, train_acc_sum/len(traindataset),test_acc_sum/len(testdataset)))\n",
    "    end_time=time.time() \n",
    "    print(\"%d轮 总用时: %.2fs\" % (epochs, end_time - begin_time))\n",
    "    return train_loss_list,test_loss_list,train_acc_list,test_acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0442b251",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0070 | test loss:0.0053 | train acc: 0.5476 | test acc: 0.6383\n",
      "epoch: 2 | train loss:0.0043 | test loss:0.0039 | train acc: 0.6733 | test acc: 0.6706\n",
      "epoch: 3 | train loss:0.0035 | test loss:0.0034 | train acc: 0.7036 | test acc: 0.7110\n",
      "epoch: 4 | train loss:0.0031 | test loss:0.0031 | train acc: 0.7357 | test acc: 0.7365\n",
      "epoch: 5 | train loss:0.0029 | test loss:0.0029 | train acc: 0.7570 | test acc: 0.7480\n",
      "epoch: 6 | train loss:0.0027 | test loss:0.0028 | train acc: 0.7706 | test acc: 0.7655\n",
      "epoch: 7 | train loss:0.0026 | test loss:0.0027 | train acc: 0.7821 | test acc: 0.7743\n",
      "epoch: 8 | train loss:0.0025 | test loss:0.0026 | train acc: 0.7913 | test acc: 0.7860\n",
      "epoch: 9 | train loss:0.0024 | test loss:0.0025 | train acc: 0.7976 | test acc: 0.7901\n",
      "epoch: 10 | train loss:0.0024 | test loss:0.0025 | train acc: 0.8028 | test acc: 0.7944\n",
      "10轮 总用时: 91.74s\n"
     ]
    }
   ],
   "source": [
    "net212=NetNN()\n",
    "momentum_optimizer=torch.optim.SGD(net212.parameters(),lr=0.01,momentum=0.5)\n",
    "trainloss212,testloss212,trainacc212,testacc212=trainNN_l2(net212,traindataloader,testdataloader,torch.nn.CrossEntropyLoss(),10,0.01,momentum_optimizer,True,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "d876cae6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0068 | test loss:0.0051 | train acc: 0.5705 | test acc: 0.6508\n",
      "epoch: 2 | train loss:0.0042 | test loss:0.0038 | train acc: 0.6732 | test acc: 0.6778\n",
      "epoch: 3 | train loss:0.0034 | test loss:0.0033 | train acc: 0.7104 | test acc: 0.7116\n",
      "epoch: 4 | train loss:0.0030 | test loss:0.0030 | train acc: 0.7410 | test acc: 0.7366\n",
      "epoch: 5 | train loss:0.0028 | test loss:0.0028 | train acc: 0.7629 | test acc: 0.7597\n",
      "epoch: 6 | train loss:0.0026 | test loss:0.0026 | train acc: 0.7780 | test acc: 0.7731\n",
      "epoch: 7 | train loss:0.0025 | test loss:0.0025 | train acc: 0.7901 | test acc: 0.7815\n",
      "epoch: 8 | train loss:0.0024 | test loss:0.0024 | train acc: 0.7984 | test acc: 0.7896\n",
      "epoch: 9 | train loss:0.0023 | test loss:0.0024 | train acc: 0.8049 | test acc: 0.7935\n",
      "epoch: 10 | train loss:0.0022 | test loss:0.0023 | train acc: 0.8112 | test acc: 0.8000\n",
      "10轮 总用时: 93.28s\n"
     ]
    }
   ],
   "source": [
    "net222=NetNN()\n",
    "momentum_optimizer=torch.optim.SGD(net222.parameters(),lr=0.01,momentum=0.5)\n",
    "trainloss222,testloss222,trainacc222,testacc222=trainNN_l2(net222,traindataloader,testdataloader,torch.nn.CrossEntropyLoss(),10,0.01,momentum_optimizer,True,1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "03316dd2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['lambda=0','lambda=2','lambda=3','Train Loss']\n",
    "train_loss=[trainloss11,trainloss221,trainloss211]\n",
    "draw(name,train_loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "2a68be11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['lambda=0','lambda=2','lambda=3','Test Loss']\n",
    "test_loss=[testloss11,testloss221,testloss211]\n",
    "draw(name,test_loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a5506d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['weight_decay=0','weight_decay=1e-5','weight_decay=0.01','Train Loss']\n",
    "train_loss=[trainloss12,trainloss222,trainloss212]\n",
    "draw(name,train_loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f7b83fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['weight_decay=0','weight_decay=1e-5','weight_decay=0.01','Test Loss']\n",
    "test_loss=[testloss12,testloss222,testloss212]\n",
    "draw(name,test_loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f6b20a1",
   "metadata": {},
   "source": [
    "## 可以看到加上了L2正则化以后损失反而大了，不过将训练集损失和测试集损失对比发现加上L2正则化以后测试集损失下降更快，说明L2正则化还是起到作用了，实验效果不明显可能是由于迭代次数过少。由于我的电脑训练时间较长，所以不能再增加迭代次数了。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd35944",
   "metadata": {},
   "source": [
    "# 在多分类任务实验中分别手动实现和用torch.nn实现dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3621f9ca",
   "metadata": {},
   "source": [
    "## 手动实现dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4101753",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net3():\n",
    "    def __init__(self,dropout=0):\n",
    "        self.dropout=dropout\n",
    "        num_inputs, num_hiddens, num_outputs = 28 * 28, 256, 10  \n",
    "        w_1 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_inputs)), dtype=torch.float32)\n",
    "        b_1 = torch.zeros(num_hiddens, dtype=torch.float32)\n",
    "        w_2 = torch.tensor(np.random.normal(0, 0.01, (num_outputs, num_hiddens)), dtype=torch.float32)\n",
    "        b_2 = torch.zeros(num_outputs, dtype=torch.float32)\n",
    "        self.params = [w_1, b_1, w_2, b_2]\n",
    "        self.w=[w_1,w_2]\n",
    "        for param in self.params:\n",
    "            param.requires_grad = True\n",
    "        self.input_layer = lambda x: x.view(x.shape[0], -1)\n",
    "        self.hidden_layer = lambda x: self.my_relu(torch.matmul(x, w_1.t()) + b_1)\n",
    "        self.output_layer = lambda x: torch.matmul(x, w_2.t()) + b_2\n",
    "\n",
    "    def my_relu(self, x):\n",
    "        return torch.max(input=x, other=torch.tensor(0.0))\n",
    "    \n",
    "    def train(self):\n",
    "        self.is_train=True\n",
    "    def test(self):\n",
    "        self.is_test=False\n",
    "    def dropout_(self,x):\n",
    "        assert 0<=self.dropout<=1\n",
    "        keep=1-self.dropout\n",
    "        if keep==0:\n",
    "            return torch.zeros_like(x)\n",
    "        mask=(torch.rand(x.shape)<keep).float()\n",
    "        return mask*x/keep\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        if self.is_train:\n",
    "            x=self.dropout_(x)\n",
    "        elif self.is_test:\n",
    "            x=self.dropout_(x)\n",
    "        x = self.my_relu(self.hidden_layer(x))\n",
    "        x = self.output_layer(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "92993b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train3(net,train_iter,test_iter,loss_func,epochs,lr,optimizer,init_states=None):\n",
    "    train_loss_list=[]\n",
    "    train_acc_list=[]\n",
    "    test_loss_list=[]\n",
    "    test_acc_list=[]\n",
    "    states=init_states(net.params)\n",
    "    begin_time=time.time()\n",
    "    net.train()\n",
    "    for epoch in range(epochs):\n",
    "        train_loss_sum,train_acc_sum,n1,c=0.0,0.0,0,0\n",
    "        for X,y in train_iter:\n",
    "            y_hat=net.forward(X)\n",
    "            l=loss_func(y_hat,y).sum()\n",
    "            l.backward()\n",
    "            optimizer(net.params,states,lr)\n",
    "            for param in net.params:\n",
    "                param.grad.data.zero_()\n",
    "            train_loss_sum+=l.item()\n",
    "            train_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()\n",
    "            n1+=y.shape[0]\n",
    "            c+=1\n",
    "        train_loss_list.append(train_loss_sum/n1)\n",
    "        train_acc_list.append(train_acc_sum/len(traindataset))\n",
    "        net.test()\n",
    "        with torch.no_grad():\n",
    "            test_loss_sum,test_acc_sum,n2,c=0.0,0.0,0,0\n",
    "            for X,y in test_iter:\n",
    "                y_hat=net.forward(X)\n",
    "                l=loss_func(y_hat,y).sum()\n",
    "                test_loss_sum+=l.item()\n",
    "                test_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()\n",
    "                n2+=y.shape[0]\n",
    "                c+=1\n",
    "            test_loss_list.append(test_loss_sum/n2)\n",
    "            test_acc_list.append(test_acc_sum/len(testdataset))\n",
    "        print('epoch: %d | train loss:%.4f | test loss:%.4f | train acc: %.4f | test acc: %.4f'\n",
    "              % (epoch + 1, train_loss_sum/n1, test_loss_sum/n2, train_acc_sum/len(traindataset),test_acc_sum/len(testdataset)))\n",
    "    end_time=time.time()\n",
    "    print(\"%d轮 总用时: %.2fs\" % (epochs, end_time - begin_time))\n",
    "    return train_loss_list,test_loss_list,train_acc_list,test_acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "53440489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0074 | test loss:0.0053 | train acc: 0.3987 | test acc: 0.5924\n",
      "epoch: 2 | train loss:0.0042 | test loss:0.0037 | train acc: 0.6379 | test acc: 0.6608\n",
      "epoch: 3 | train loss:0.0033 | test loss:0.0032 | train acc: 0.6830 | test acc: 0.6923\n",
      "epoch: 4 | train loss:0.0030 | test loss:0.0030 | train acc: 0.7210 | test acc: 0.7267\n",
      "epoch: 5 | train loss:0.0028 | test loss:0.0028 | train acc: 0.7480 | test acc: 0.7437\n",
      "epoch: 6 | train loss:0.0026 | test loss:0.0027 | train acc: 0.7622 | test acc: 0.7616\n",
      "epoch: 7 | train loss:0.0025 | test loss:0.0026 | train acc: 0.7739 | test acc: 0.7666\n",
      "epoch: 8 | train loss:0.0025 | test loss:0.0025 | train acc: 0.7804 | test acc: 0.7712\n",
      "epoch: 9 | train loss:0.0024 | test loss:0.0025 | train acc: 0.7833 | test acc: 0.7741\n",
      "epoch: 10 | train loss:0.0024 | test loss:0.0025 | train acc: 0.7894 | test acc: 0.7789\n",
      "10轮 总用时: 106.14s\n"
     ]
    }
   ],
   "source": [
    "net311=Net3(0.5)\n",
    "momentum=0.5\n",
    "trainloss311,testloss311,trainacc311,testacc311=train3(net311,traindataloader,testdataloader,my_cross_entropy_loss,10,0.01,sgd_momentum,init_momentum_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c52f959a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0076 | test loss:0.0056 | train acc: 0.3517 | test acc: 0.5545\n",
      "epoch: 2 | train loss:0.0045 | test loss:0.0040 | train acc: 0.6069 | test acc: 0.6306\n",
      "epoch: 3 | train loss:0.0036 | test loss:0.0036 | train acc: 0.6556 | test acc: 0.6620\n",
      "epoch: 4 | train loss:0.0034 | test loss:0.0034 | train acc: 0.6856 | test acc: 0.6876\n",
      "epoch: 5 | train loss:0.0032 | test loss:0.0032 | train acc: 0.7094 | test acc: 0.7017\n",
      "epoch: 6 | train loss:0.0031 | test loss:0.0031 | train acc: 0.7195 | test acc: 0.7186\n",
      "epoch: 7 | train loss:0.0030 | test loss:0.0030 | train acc: 0.7297 | test acc: 0.7188\n",
      "epoch: 8 | train loss:0.0029 | test loss:0.0030 | train acc: 0.7336 | test acc: 0.7264\n",
      "epoch: 9 | train loss:0.0029 | test loss:0.0030 | train acc: 0.7345 | test acc: 0.7297\n",
      "epoch: 10 | train loss:0.0028 | test loss:0.0029 | train acc: 0.7413 | test acc: 0.7331\n",
      "10轮 总用时: 131.67s\n"
     ]
    }
   ],
   "source": [
    "net321=Net3(0.8)\n",
    "momentum=0.5\n",
    "trainloss321,testloss321,trainacc321,testacc321=train3(net321,traindataloader,testdataloader,my_cross_entropy_loss,10,0.01,sgd_momentum,init_momentum_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d38b48b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cAWgclIyIrAPAgk0nSWk1DNROcGEXHDV/m7Nfw364adw4kXiCn0/9bM9yhRBCCFEBnT59usK1S5QWCcQVxNNNq1OrmjOJ6dks2p0G4QPNBza8B0YDbho3Xmn0CgDzYueRZciyY7VCCCGEEBVHuQjE8+bNIygoCJ1OR3h4ODt27Chy/KpVq6hXrx46nY5GjRpZrXkHYDKZmDhxIn5+fjg6OhIZGcnx48ctx//66y8UCoXNbefOnWXyGe+WSqlg1CPmu9B88fcpbjQdBFp3uHwIDnwPQM96PfFx8iE+LZ4VR1bYs1whhBBCiArD7oF45cqVjBw5kkmTJrF7926aNGlCVFQUly9ftjl+69at9OrVi/79+7Nnzx66detGt27dOHDggGXMtGnTmD17NgsWLGD79u04OzsTFRVFZmYmAG3atOHSpUtW28svv0xwcHCBWx2WJ4+G+hLq70aa3sD87deg7TDzgY3vgyEbnYOO18JeA2DR/kWk6FPsWK0QQgghRMVg90A8c+ZMXnnlFfr160eDBg1YsGABTk5OfPnllzbHf/LJJzz66KOMGTOG+vXrM3nyZJo1a8bcuXMB8+zwrFmzGD9+PE899RSNGzfmq6++4uLFi6xZswYAjUaDr6+vZfPy8uLHH3+kX79+5XrJMqVSwejcWeKlW09zuUE/cK4GN+IgdhkAT9Z6kmD3YJKyklhycIkdqxVCCCGEqBjsGoj1ej27du0iMjLSsk+pVBIZGUlMTIzNc2JiYqzGA0RFRVnGx8XFER8fbzXG3d2d8PDwQq/5008/ce3aNfr161dorVlZWSQnJ1tt9tCxbjVaBHqSlWNk9j8Xof0o84G/pkJ2Jg5KB4Y3Nd9R5utDX3M146pd6hRCCCGEqCjsGoivXr2KwWDAx8fHar+Pjw/x8fE2z4mPjy9yfN7jnVzziy++ICoqiho1ahRa65QpU3B3d7dsAQEBRX+4MqJQKBgdZZ4lXrHjHGeDe4BbDUi5CP9+AcDDNR+mcbXGZORksGDvArvUKYQQQghRUdi9ZcLezp8/zx9//EH//v2LHPfmm2+SlJRk2c6dO3ePKiyo9QNetA+pSo7RxKxNZ6HDG+YDf8+ErBQUCgUjmo0AYPWx1ZxNPmu3WoUQQghR9jp27Fhpl0wrDXYNxFWrVkWlUpGQkGC1PyEhAV9fX5vn+Pr6Fjk+77G411y8eDFeXl48+eSTRdaq1Wpxc3Oz2uwpb8WJNXsucML/CahSC9KvwjbzjHBL35a0q96OHFMOc/fMtWepQgghhKik9u3bR/v27dHpdAQEBDBt2rTbnmNrFbAVK8p29Sy7BmKNRkPz5s2Jjo627DMajURHRxMREWHznIiICKvxAOvXr7eMDw4OxtfX12pMcnIy27dvL3BNk8nE4sWL6d27N2q1urQ+1j0RFuDBIw18MJpg5oY4eGic+cDW2ZB+HYDhzcy9xL+f/p1D1w7Zq1QhhBBC2JFer7fL+yYnJ/PII48QGBjIrl27mD59Om+//TYLFy687bmLFy+2Wg2sW7duZVqr3VsmRo4cyaJFi1i6dCmHDx9m0KBBpKWlWb7g1rt3b958803L+OHDh7N27VpmzJjBkSNHePvtt/n3338ZMmQIYP5bxYgRI3jvvff46aef2L9/P71798bf37/AD3PDhg3ExcXx8ssv37PPW5pGPVIXhQJ+2x/PAc9O4B0KWcnmUAzUq1KPrsFdAZi9e7Y9SxVCCCHKN5MJ9Gn22UymOyo1LS2N3r174+Ligp+fHzNmzLA6HhQUxOTJk+nduzdubm4MGDAAgNWrVxMaGopWqyUoKKjQ83r16oWzszPVq1dn3rx5Jf6RLlu2DL1ez5dffkloaCg9e/Zk2LBhzJw587bnenh4WK0IptPpSlxHcTiU6dWLoUePHly5coWJEycSHx9PWFgYa9eutXwp7uzZsyiVN3N7mzZtWL58OePHj2fcuHGEhISwZs0aGjZsaBnzxhtvkJaWxoABA0hMTKRdu3asXbu2wA/ziy++oE2bNtSrV+/efNhSVtfXlSeb+PNj7EU+Wn+cJZ0mwP96mtsmwgeBqw9Dmg5h3Zl1bLm4hR2XdtDKr5W9yxZCCCHKn+x0+MDfPu897iJonIs9fMyYMWzatIkff/wRb29vxo0bx+7duwkLC7OM+eijj5g4cSKTJk0CYNeuXXTv3p23336bHj16sHXrVl577TW8vLzo27ev5bzp06czbtw43nnnHf744w+GDx9OnTp16Ny5MwBdunTh77//LrS2wMBADh48CJhXBnvwwQfRaDSW41FRUUydOpUbN27g6elZ6HUGDx7Myy+/zAMPPMDAgQPLfGlchcl0h38tEYD5nwHc3d1JSkqyaz/x6atpdJq5CYPRxHevtqbFn93hwr/Q6lXoau7T+WD7B/zvyP9oVLURy7ouK9drLQshhBBlLTMzk7i4OIKDg29OlunTKkQgTk1NxcvLi2+++Ybnn38egOvXr1OjRg0GDBjArFmzCAoKomnTpvzwww+W81588UWuXLnCunXrLPveeOMNfv31V0uADQoKon79+vz++++WMT179iQ5OdlyV+ALFy6QkZFRaH1qtZrAwEAAHnnkEYKDg/nss88sxw8dOkRoaCiHDh2ifv36Nq8xefJkHn74YZycnFi3bh2TJk1i2rRpDBs2zOZ4m7/PXMXNa3afIRZ3J6iqM91b1OB/O84xbd0xVnaegOKrp+DfL6HNEPCoyYDGA1hzYg37r+7nz7N/0jmws73LFkIIIcoXtZM5mNrrvYvp5MmT6PV6wsPDLfuqVKlC3bp1rcbdeufdw4cP89RTT1nta9u2LbNmzcJgMKBSqQAKfN8qIiKCWbNmWV5Xr1692LWW1IQJEyzPmzZtSlpaGtOnTy80EJcGu/cQi7s39OEQNColO+Ku83dOKAR3AGO2+WYdQFXHqvQJ7QOYe4lzjDn2LFcIIYQofxQK8yytPbYy+JdbZ+fit2DciS5duuDi4lLoFhoaahlb2MpgeceKKzw8nPPnz5OVlVU6H8IGCcT3AX8PR15qbf7niY/WHcX0cO7frPYuh6vHAejToA+eWk9OJ59mzYk1dqpUCCGEEHejVq1aqNVqtm/fbtl348YNjh07VuR59evXZ8uWLVb7tmzZQp06dSyzwwDbtm2zGrNt2zar1obPP/+c2NjYQre81gowzy5v3ryZ7Oxsy77169dTt27dIvuHbxUbG4unpydarbbY59wpCcT3idceqoWTRsW+80msSw6Aul3BZISN7wPgonHhlcavADA/dj6ZOZn2LFcIIYQQJeDi4kL//v0ZM2YMGzZs4MCBA/Tt29dqAQJbRo0aRXR0NJMnT+bYsWMsXbqUuXPnMnr0aKtxW7ZsYdq0aRw7dox58+axatUqhg8fbjlevXp1ateuXeiW1z8M8MILL6DRaOjfvz8HDx5k5cqVfPLJJ4wcOdIy5ocffrBa3ODnn3/m888/58CBA5w4cYL58+fzwQcfMHTo0Lv90RVJAvF9oqqLln5tgwCYue4Yho7jAAUc/AEu7QOgR90e+Dv7cznjMsuPLLdfsUIIIYQosenTp9O+fXueeOIJIiMjadeuHc2bNy/ynGbNmvHtt9+yYsUKGjZsyMSJE3n33XetVpgAc3D+999/adq0Ke+99x4zZ84kKiqqRHW6u7uzbt064uLiaN68OaNGjWLixImWZeAAkpKSOHr0qOW1Wq1m3rx5REREEBYWxmeffcbMmTMtq2WUFVllooTKyyoT+SWlZ9Nu2gZSMnOY1SOMbicnwoHvICQKXvwWgJ9O/sRb/7yFq8aV35/5HXetu52rFkIIIe6tolYlqMyCgoIYMWJEhbsFdGmsMiEzxPcRdyc1AzvUAuDjP4+R/eBYUKjg+B9w1txr9FjwY9T2qE2KPoUvD3xpz3KFEEIIIcoFCcT3mb5tgqjqouHMtXS+O62Fpi+aD0S/CyYTKqWKEc1GALDs8DIS0hIKv5gQQgghRCUggfg+46x1YFDH2gDMjj5OZtvRoNLAmX/g1EYAHqzxIE29m5JlyGL+3vn2LFcIIYQQ5cTp06crXLtEaZFAfB96Mbwmfu46LiVlsuywEVr0Nx/InSVWKBS83vx1ANacWENcUpwdqxVCCCGEsC8JxPchnVrFsE4hAHy68QRp4cNA7QwX98CRXwFo6t2UjjU6YjAZmLNnjj3LFUIIIYSwKwnE96nnmtcg0MuJa2l6FsemQetB5gMb3gOjAYBhzYahQMH6M+s5cPWAHasVQgghhLAfCcT3KbVKycjOdQD4bPMpkpoOAp07XDkMB1YDEOIZwhO1ngBg1q5ZyAp8QgghhKiMJBDfx55o7E9dH1dSMnNYuPMqtM2908zG98Fgvo3i4LDBqJVqtsdvJ+ZijB2rFUIIIYSwDwnE9zGlUsHIR8yzxIu3nOZKaD9wrgY3TsOerwHwd/GnR90eAMzaPQujyWivcoUQQggh7EIC8X3ukQY+NKnhTrrewKdbLkH73HuWb5oO2RkADGg8AGe1M4evH+aP03/YsVohhBBClETHjh0r7ZJppUEC8X1OoVAwOqouAMu2neVi7Z7gVgNSLsLOLwDw1HnSN7QvAHP2zCHbmG2vcoUQQghxH9m3bx/t27dHp9MREBDAtGnTbnvOzp076dSpEx4eHnh6ehIVFcXevXvLtE4JxJVAu9pVCQ+ugt5gZM7ms9Dxv+YD/8yErBQAejfojZfOi3Mp5/j+2Pd2rFYIIYQQpUmv19vlfZOTk3nkkUcIDAxk165dTJ8+nbfffpuFCxcWek5qaiqPPvooNWvWZPv27fzzzz+4uroSFRVFdnbZTdhJIK4EFAoFY3Jnib/99zxxNZ6CKrUg/RpsM9+pzkntxKtNXgVgwb4FpGen261eIYQQ4l4zmUykZ6fbZbvTVZ7S0tLo3bs3Li4u+Pn5MWPGDKvjQUFBTJ48md69e+Pm5saAAQMAWL16NaGhoWi1WoKCggo9r1evXjg7O1O9enXmzZtX4p/psmXL0Ov1fPnll4SGhtKzZ0+GDRvGzJkzCz3nyJEjXL9+nXfffZe6desSGhrKpEmTSEhI4MyZMyWu5XYcyuzKolxpEVSFh+pWY+PRK8zacIpPHn4Lvvs/2DoHWr4MTlV4LuQ5vjr4FedTz/PN4W8Y0HiAvcsWQggh7omMnAzCl4fb5b23v7AdJ7VTscePGTOGTZs28eOPP+Lt7c24cePYvXs3YWFhljEfffQREydOZNKkSQDs2rWL7t278/bbb9OjRw+2bt3Ka6+9hpeXF3379rWcN336dMaNG8c777zDH3/8wfDhw6lTpw6dO3cGoEuXLvz999+F1hYYGMjBgwcBiImJ4cEHH0Sj0ViOR0VFMXXqVG7cuIGnp2eB8+vWrYuXlxdffPEF48aNw2Aw8MUXX1C/fn2CgoKK/TO6UxKIK5FRj9Rl49Er/LT3IoM6dKKeTyNI2A9bPoHO76BWqRnSdAhj/x7L4gOL6V6nOx46D3uXLYQQQohcqampfPHFF3zzzTd06tQJgKVLl1KjRg2rcQ8//DCjRo2yvH7xxRfp1KkTEyZMAKBOnTocOnSI6dOnWwXitm3bMnbsWMuYLVu28PHHH1sC8eeff05GRkah9anVasvz+Ph4goODrY77+PhYjtkKxK6urvz1119069aNyZMnAxASEsIff/yBg0PZxVYJxJVIw+rudG3ky2/745mx/gSLHh4P/+sB2z8z38nO1ZcuwV1YcnAJR64fYdH+RYxpOcbeZQshhBBlztHBke0vbLfbexfXyZMn0ev1hIffnM2uUqUKdevWtRrXokULq9eHDx/mqaeestrXtm1bZs2ahcFgQKVSARAREWE1JiIiglmzZlleV69evdi1lkRGRgb9+/enbdu2/O9//8NgMPDRRx/x2GOPsXPnThwdi/+zuhPSQ1zJjOxcB6UC1h9KINYxHGq0gpwM2PwRAEqFkuHNzDfwWHFkBZdSL9mzXCGEEOKeUCgUOKmd7LIpFIpS/zzOzs6lfk0wt0y4uLgUuoWGhlrG+vr6kpCQYHV+3mtfX1+b11++fDmnT59m8eLFtGzZktatW7N8+XLi4uL48ccfy+QzgQTiSqe2tytPNzX/s8qM9cegk/mfTti1BG6Ym9Xb+relpW9L9EY9n+791E6VCiGEEOJWtWrVQq1Ws337zdnsGzducOzYsSLPq1+/Plu2bLHat2XLFurUqWOZHQbYtm2b1Zht27ZRv359y+vPP/+c2NjYQrfffvvNMjYiIoLNmzdbrQ6xfv166tata7NdAiA9PR2lUmn1l4S810Zj2d08TAJxJTQiMgS1SsHfx68SYwyFBzqCMRs2TQXMf0se0WwEAD+d/ImTiSftV6wQQgghLFxcXOjfvz9jxoxhw4YNHDhwgL59+6JUFh3pRo0aRXR0NJMnT+bYsWMsXbqUuXPnMnr0aKtxW7ZsYdq0aRw7dox58+axatUqhg8fbjlevXp1ateuXegWGBhoGfvCCy+g0Wjo378/Bw8eZOXKlXzyySeMHDnSMuaHH36gXr16ltedO3fmxo0bDB48mMOHD3Pw4EH69euHg4MDDz300N3++AolgbgSCqjiRI+WAQB8tO4opodzZ4n3/g+umP+G2bhaYyJrRmI0Gflk9yf2KlUIIYQQt5g+fTrt27fniSeeIDIyknbt2tG8efMiz2nWrBnffvstK1asoGHDhkycOJF3333X6gt1YA7O//77L02bNuW9995j5syZREVFlahOd3d31q1bR1xcHM2bN2fUqFFMnDjRsgwcQFJSEkePHrW8rlevHj///DP79u0jIiKC9u3bc/HiRdauXYufn1+J6igOhelOF78TgHmxaXd3d5KSknBzc7N3OXcsITmTB6dtJCvHyOK+LXlozwg4+is06AbdlwJwKvEUT//0NEaTka+7fE2Yd5g9SxZCCCFKRWZmJnFxcQQHB6PT6exdTrkRFBTEiBEjKtwtoIv6fRY3r9l9hnjevHkEBQWh0+kIDw9nx44dRY5ftWoV9erVQ6fT0ahRI6teFTAvrD1x4kT8/PxwdHQkMjKS48ePF7jOr7/+Snh4OI6Ojnh6etKtW7fS/Fjlno+bjj5tggDzLLGx4zhAAYfWwCXz7REf8HiAbrW7AfDxro/veOFwIYQQQoiKwK6BeOXKlYwcOZJJkyaxe/dumjRpQlRUFJcvX7Y5fuvWrfTq1Yv+/fuzZ88eunXrRrdu3Thw4IBlzLRp05g9ezYLFixg+/btODs7ExUVRWZmpmXM6tWr+c9//kO/fv3Yu3cvW7Zs4YUXXijzz1veDOxQCxetAwcvJvP7FS9o9Jz5wIb3LGMGNRmEVqVl9+Xd/H2h8IW4hRBCCCEqKru2TISHh9OyZUvmzp0LgNFoJCAggKFDh1oWhc6vR48epKWl8csvv1j2tW7dmrCwMBYsWIDJZMLf359Ro0ZZmsSTkpLw8fFhyZIl9OzZk5ycHIKCgnjnnXfo379/iWuv6C0TeT5ef4xPoo9Tq5oz6/oEoJrXEkwG6LcWAs1rEc78dyaLDy6mjmcdVj2xCqXC7v+wIIQQQpSYtEzcXyp0y4Rer2fXrl1ERkbeLEapJDIykpiYGJvnxMTEWI0H8y0A88bHxcURHx9vNcbd3Z3w8HDLmN27d3PhwgWUSiVNmzbFz8+PLl26WM0y25KVlUVycrLVdj94uX0wHk5qTl5J44czWmj2H/OBDZMh9+9K/Rv1x1XjyrEbx/j11K92rFYIIYQQovTZLRBfvXoVg8FguYVfHh8fH+Lj422eEx8fX+T4vMeixpw6dQqAt99+m/Hjx/PLL7/g6elJx44duX79eqH1TpkyBXd3d8sWEBBwB5+2/HLVqRnYoRYAs/48hr7taFBp4cwWOLkBAHetO//X8P8AmBc7D71Bb7d6hRBCiNIi3425P5TG77HS/dt33qLOb731Fs8++yzNmzdn8eLFKBQKVq1aVeh5b775JklJSZbt3Llz96rkMtcnIohqrlrO38hg5VEDtHzZfCDfLPGL9V+kmmM1LqReYNWxwn9OQgghRHmnVqsB800gRMWX93vM+72WhENpFXOnqlatikqlsnlLv8Ju51fYLQDzxuc9JiQkWK1Vl5CQQFhYGIBlf4MGDSzHtVotDzzwAGfPni20Xq1Wi1arLeanq1gcNSqGPlybiT8eZM6GEzz32jAcdy2Bi3vgyC9Q/wkcHRwZ2GQgk7dNZuG+hXSr3Q1nddncFlIIIYQoSyqVCg8PD8uX+J2cyub2yaJsmUwm0tPTuXz5Mh4eHlZ33LtTdgvEGo2G5s2bEx0dbVnyzGg0Eh0dzZAhQ2yeExERQXR0tNX6eOvXryciwvzlr+DgYHx9fYmOjrYE4OTkZLZv386gQYMAaN68OVqtlqNHj9KuXTsAsrOzOX36tNXdVSqbni1r8tmmU1xIzODr/WkMiHgNNk83rzhRtysoVTwd8jRfHfqKM8ln+OrgVwwKG2TvsoUQQogSyZtEK2xlK1FxeHh4FDqZWlx2C8QAI0eOpE+fPrRo0YJWrVoxa9Ys0tLS6NevHwC9e/emevXqTJkyBYDhw4fToUMHZsyYwWOPPcaKFSv4999/WbhwIZB7y+ERI3jvvfcICQkhODiYCRMm4O/vbwndbm5uDBw4kEmTJhEQEEBgYCDTp08H4Pnnn7/3P4RyQuOgZHhkCG98t49P/zpJr2EDcd2xCK4cgf2roElP1Eo1Q5sOZfSm0Sw5uITudbvj5ehl79KFEEKIO6ZQKPDz88Pb25vs7Gx7lyNKSK1W39XMcB67BuIePXpw5coVJk6cSHx8PGFhYaxdu9bypbizZ89a3Zu7TZs2LF++nPHjxzNu3DhCQkJYs2YNDRs2tIx54403SEtLY8CAASQmJtKuXTvWrl1rtQzH9OnTcXBw4D//+Q8ZGRmEh4ezYcMGPD09792HL4eeaVqdBZtOcupKGl/8e50RbYdD9Dvw1xQIfQYcNDwS+AihXqEcvHaQRfsXMbZVweXxhBBCiIpCpVKVSqASFZvcurmE7pd1iG/1y76LDFm+BxetA3+PaIXn560g7TI8NhNamtdt3nZpG6+sewUHpQM/d/uZGq417Fy1EEIIIURB5X4dYlE+dW3oR30/N1KzclgQEw8PjjEf2DwdsjMAaO3XmtZ+rckx5jAvdp4dqxVCCCGEuHsSiIUVpVLBmKg6ACyNOc3lkB7gHgApl2Dn55ZxI5qPAODXU79y9PpRe5QqhBBCCFEqJBCLAh6q602zmh5kZhuZ+/c56JjbJ/z3TMg036Ev1CuUqKAoTJiYvWe2HasVQgghhLg7EohFAQqFgtFRdQH4346znAt4ErxCIOM6bJtvGTe06VAcFA5sPr+ZXQm77FWuEEIIIcRdkUAsbGpTqypta3uRbTDxycY4eGic+cDWOZBuvsV1oFsgz4Q8A8DHuz6WW2AKIYQQokKSQCwKNfoR8yzx97vPc6JaJPg2An0K/POxZczAJgPRqXTsvbKXjec22qtUIYQQQogSk0AsCtW0pieR9X0wmuDj6BPw8ATzgR2LIPkSANWcqvFSg5cAmL17NgajwV7lCiGEEEKUiARiUaRRj9RBoYBf913igFM4BIRDTgb8/ZFlTL+G/XDTuHEy6SQ/nfzJjtUKIYQQQtw5CcSiSPX93HiisT8AM/88Dp0mmg/sWgo3TgPgpnHjlUavAPDp3k/JMmTZo1QhhBBCiBKRQCxu6/XOdVApFWw4cpldigbwwENgzIa/plrG9KrfCx8nH+LT4llxZIUdqxVCCCGEuDMSiMVtBVd15rlm5tszT//jKKa8XuJ9K+CK+aYcWpWWwWGDAVi0fxEp+hS71CqEEEIIcackEItiGRYZgkalZNup62zJCIR6j4PJCBvft4x5otYTPOD+AElZSSw+sNiO1QohhBBCFJ8EYlEs1T0ceSG8JgDT1x3F9NA4QAGHfoSLewBwUDowrOkwAL45/A1X0q/Yq1whhBBCiGKTQCyKbfBDtXFUq9h7LpE/r1WFxt3NBza8ZxnzcM2HaVytMRk5GXy27zM7VSqEEEIIUXwSiEWxVXPV0rdtEAAz1h3F+OBYUDrAiT/hzFbAfNvnEc1GALD62GrOJp+1U7VCCCGEEMUjgVjckVcffABXnQNH4lP4+bwWmv7HfCB6MuTeurmlb0vaVW9HjimHuXvm2rFaIYQQQojbk0As7oiHk4YB7R8A4OP1x8huNxpUWji7FU5GW8aNaDYCBQp+P/07h64dsle5QgghhBC3JYFY3LF+7YKp4qzh9LV0Vh83QivzTTmIftcyS1y3Sl26PtAVgE92f2KvUoUQQgghbksCsbhjLloHXutYC4DZ0cfJaj0MNC5waS8cvnnr5sFhg3FQOrD14la2X9pur3KFEEIIIYokgViUyEutA/F103ExKZPlB9Kh9WvmAxveB6MBgADXAJ6v8zwAs3bNwpQ7eyyEEEIIUZ5IIBYlolOrGNqpNgDzNp4gvcUg0HnA1aOw71vLuAGNB+Do4MiBawdYf2a9naoVQgghhCicBGJRYt1bBFCzihNXU/Us3nUd2r1uPvDXFMjRA1DVsSp9QvsAMGfPHHKMOfYqVwghhBDCJgnEosTUKiWvdw4B4LNNJ0lq3A9cfCDxDOz5yjKuT4M+eGo9OZ18mjUn1tipWiGEEEII2yQQi7vyZJPqhHi7kJyZw+fb4uHBMeYDm6aDPh0AF40LAxoPAGB+7HwycjLsVa4QQgghRAESiMVdUSkVjHqkDgBf/BPH1bo9waMmpMbDzs8t47rX7Y6/sz+XMy6z/PBye5UrhBBCCFGABGJx16JCfWlU3Z10vYH5f5+DDmPNB/6ZCZnJAGhUGgY3HQzAFwe+ICkryV7lCiGEEEJYKVEgPnfuHOfPn7e83rFjByNGjGDhwoWlVpioOBQKBaOj6gLw9bYzXAp6EqrWgYwbEDPPMu6x4Meo7VGbFH0KXxz4wl7lCiGEEEJYKVEgfuGFF9i4cSMA8fHxdO7cmR07dvDWW2/x7rvv3vH15s2bR1BQEDqdjvDwcHbs2FHk+FWrVlGvXj10Oh2NGjXit99+szpuMpmYOHEifn5+ODo6EhkZyfHjx63GBAUFoVAorLYPP/zwjmsXZg+GVKVVUBX0OUZmbzwND40zH4iZB2nXAFApVYxoNgKA5YeXk5CWYJ9ihRBCCCHyKVEgPnDgAK1atQLg22+/pWHDhmzdupVly5axZMmSO7rWypUrGTlyJJMmTWL37t00adKEqKgoLl++bHP81q1b6dWrF/3792fPnj1069aNbt26ceDAAcuYadOmMXv2bBYsWMD27dtxdnYmKiqKzMxMq2u9++67XLp0ybINHTr0zn4QwiL/LPGqf89xxicSfBuDPgW2fGwZ92CNB2nm3YwsQxbz9863V7lCCCGEEBYlCsTZ2dlotVoA/vzzT5588kkA6tWrx6VLl+7oWjNnzuSVV16hX79+NGjQgAULFuDk5MSXX35pc/wnn3zCo48+ypgxY6hfvz6TJ0+mWbNmzJ07FzDPDs+aNYvx48fz1FNP0bhxY7766isuXrzImjVrrK7l6uqKr6+vZXN2dr7Dn4TIr1VwFTrUqUaO0cSs6JPQaaL5wI5FkGz+70KhUDCi+QgA1pxYQ1xSnJ2qFUIIIYQwK1EgDg0NZcGCBfz999+sX7+eRx99FICLFy/i5eVV7Ovo9Xp27dpFZGTkzYKUSiIjI4mJibF5TkxMjNV4gKioKMv4uLg44uPjrca4u7sTHh5e4JoffvghXl5eNG3alOnTp5OTU/hNI7KyskhOTrbaREGjHzHPEq+JvcBRl3AIaA05mbB5umVMU++mdKzREYPJwJw9c+xVqhBCCCEEUMJAPHXqVD777DM6duxIr169aNKkCQA//fSTpZWiOK5evYrBYMDHx8dqv4+PD/Hx8TbPiY+PL3J83uPtrjls2DBWrFjBxo0befXVV/nggw944403Cq11ypQpuLu7W7aAgIBif87KpFENdx4N9cVkgpl/Hrs5S7x7KVy/ORs8rNkwFChYf2Y9+6/st1O1QgghhBAlDMQdO3bk6tWrXL161aq1YcCAASxYsKDUiitLI0eOpGPHjjRu3JiBAwcyY8YM5syZQ1ZWls3xb775JklJSZbt3Llz97jiimPUI3VQKOCPgwnscwiFWp3AmAObplrGhHiG8EStJwCYtXsWJpPJXuUKIYQQopIrUSDOyMggKysLT09PAM6cOcOsWbM4evQo3t7exb5O1apVUalUJCRYrzaQkJCAr6+vzXN8fX2LHJ/3eCfXBAgPDycnJ4fTp0/bPK7VanFzc7PahG0hPq48HVYdgI/WHYOHx5sP7F0Bl49Yxg0OG4xaqWZH/A5iLtpukRFCCCGEKGslCsRPPfUUX331FQCJiYmEh4czY8YMunXrxvz5xV85QKPR0Lx5c6Kjoy37jEYj0dHRRERE2DwnIiLCajzA+vXrLeODg4Px9fW1GpOcnMz27dsLvSZAbGwsSqXyjgK9KNyIyDo4KBVsPnaF7VmBUP8JwAQb37eM8Xfxp2e9noB5lthoMtqpWiGEEEJUZiUKxLt376Z9+/YAfPfdd/j4+HDmzBm++uorZs+efUfXGjlyJIsWLWLp0qUcPnyYQYMGkZaWRr9+/QDo3bs3b775pmX88OHDWbt2LTNmzODIkSO8/fbb/PvvvwwZMgTIXcVgxAjee+89fvrpJ/bv30/v3r3x9/enW7dugPmLebNmzWLv3r2cOnWKZcuW8frrr/PSSy9ZZr3F3anp5USPluY+64/WHcXUcRyggMM/wYXdlnGvNHoFZ7Uzh68f5o/Tf9ipWiGEEEJUZiUKxOnp6bi6ugKwbt06nnnmGZRKJa1bt+bMmTN3dK0ePXrw0UcfMXHiRMLCwoiNjWXt2rWWL8WdPXvWaim3Nm3asHz5chYuXEiTJk347rvvWLNmDQ0bNrSMeeONNxg6dCgDBgygZcuWpKamsnbtWnQ6HWBuf1ixYgUdOnQgNDSU999/n9dff13utFfKhj4cgtZByc7TN9iUWBUa9zAf2PCeZYynzpO+oX0BmLNnDtmGbDtUKoQQQojKTGEqwbeZGjduzMsvv8zTTz9Nw4YNWbt2LREREezatYvHHnus0BUi7ifJycm4u7uTlJQk/cRFeO+XQ3z+TxwNq7vx84s1UMxtYf6CXd/fIKgtAOnZ6XT9vivXMq/xVvhbljYKIYQQQoi7Udy8VqIZ4okTJzJ69GiCgoJo1aqVpTd33bp1NG3atGQVi/vSoI61cNaoOHAhmbUXdNCst/nAhsmQ+3cxJ7UTrzZ5FYAFexeQnp1ur3KFEEIIUQmVKBA/99xznD17ln///Zc//rjZ99mpUyc+/vjjIs4UlY2Xi5b+7YIBmLH+GIZ2o8FBB2dj4MSflnHPhTxHDZcaXMu8xjeHv7FXuUIIIYSohEoUiMG8vFnTpk25ePEi58+fB6BVq1bUq1ev1IoT94eXH3wAd0c1Jy6nsuakCVq9Yj4Q/S4YzStLqFVqhjYdCsDiA4tJzEy0U7VCCCGEqGxKFIiNRiPvvvsu7u7uBAYGEhgYiIeHB5MnT8ZolKWzhDU3nZpXOzwAwKzoY+hbDweNK8TvM686kevR4EepV6UeqdmpLNq/yF7lCiGEEKKSKVEgfuutt5g7dy4ffvghe/bsYc+ePXzwwQfMmTOHCRMmlHaN4j7Qt00QVV20nLuewbeH0iFisPnAxvfBaABAqVAyvNlwAP535H9cSr1U2OWEEEIIIUpNiQLx0qVL+fzzzxk0aBCNGzemcePGvPbaayxatIglS5aUconifuCkcWDIQ7UAmLPhOJktB4KjJ1w9BvtWWsa19W9LS9+WZBuzmRc7z17lCiGEEKISKVEgvn79us1e4Xr16nH9+vW7Lkrcn3qF16S6hyMJyVl8vfsGtHvdfOCvKZCjB3JvrNJsBAA/n/qZEzdO2KlaIYQQQlQWJQrETZo0Ye7cuQX2z507l8aNG991UeL+pHVQMbxTCADzN50ktUk/cPGFxLOwe6llXONqjYmsGYnRZGT2nju786EQQgghxJ0qUSCeNm0aX375JQ0aNKB///7079+fBg0asGTJEj766KPSrlHcR55pVp0HqjpzPU3Pl9sT4MHR5gObp4P+5vrDQ5sNRalQsvHcRmIvx9qnWCGEEEJUCiUKxB06dODYsWM8/fTTJCYmkpiYyDPPPMPBgwf5+uuvS7tGcR9xUCkZ0bkOAIs2nyKxfi/wqAmpCbDz5soSD7g/wNO1nwbg410fU4IbKgohhBBCFEuJbt1cmL1799KsWTMMBkNpXbLckls3l5zRaKLr7L85Ep/CwA61GOu3B9bkfslu+F7QuQMQnxbP4z88TpYhi4kRE3ku5DkUCoWdqxdCCCFERVGmt24W4m4olQpGP1IXgCVb47gc/CRUrQsZNyDm5soSvs6+vFD/BQDejXmXHr/0YPP5zTJbLIQQQohSJYFY2EWn+t6EBXiQmW3k002n4eG3zAdi5kHaNcu4oWFDGdB4AE4OThy+fpjB0YN56feXiLkYI8FYCCGEEKVCArGwC4VCwZgo8yzxsu1nOO8bCX5NQJ8K/8y0jMu7pfPaZ9fSL7QfOpWOfVf2MWD9AP7vj/9jV8Iue30EIYQQQtwn7qiH+JlnninyeGJiIps2bZIeYlFsLyzaxtaT1+jeogbTmlyBZc+CSgvDY8HNv8D4qxlX+Xz/53x79FuyjdkARPhFMKTpEBpXkyX/hBBCCHFTmfQQu7u7F7kFBgbSu3fvuy5eVB6jc2eJV+++wEn3cKjZBgxZ5mXYbKjqWJWxrcby2zO/8Xyd53FQOBBzKYYXf3uRIdFDOHzt8L0sXwghhBD3gVJdZaIykRni0tN/yU6ij1zm8cZ+zG2bCYu7gNIBhvwLVYKLPPd8ynk+2/cZP538CaPJCEDnwM681uQ1anvWvhflCyGEEKKcklUmRIUxKnfFiV/2XeKQuiHUjgRjDvz14W3PreFag8ltJ/PjUz/SNbgrChSsP7OeZ356hjc2v8HppNNlXL0QQgghKjoJxMLuGvi78XhjPwBmrj8KD483H9i3Ei4XrwUiyD2IqQ9O5fsnv6dzYGdMmPg97nee+vEpxv8znvMp58uqfCGEEEJUcBKIRbnweuc6KBXw5+HL7M4JgvpPAibY8N4dXae2Z21mdpzJt49/S8caHTGajPx48kee+OEJ3o15l/i0+DKpXwghhBAVlwRiUS7UqubCc81rAPDRH0fhobdAoYQjv8CFO19arb5XfeZ0msOyrsto49+GHFMOq46touv3XZmyfQpX0q+U9kcQQgghRAUlgViUG8M6haBWKdh68hpbk6tC4x7mA3c4S5xf42qN+azzZyx5dAktfFqQbcxm+ZHldP2+KzP+ncH1zOulVL0QQgghKioJxKLcqOHpxAutagIwfd1RTB3+C0o1nNwAR369q2s392nOl1FfsuiRRTSp1oRMQyZLDi6hy+ouzN49m6SspNL4CEIIIYSogCQQi3Jl8MO10amV7DmbSHS8EzTvYz6w4gVY1h0uxpb42gqFgtZ+rfm6y9fM6zSP+lXqk56TzqL9i+iyugsL9i4gVZ9aOh9ECCGEEBWGrENcQrIOcdn58PcjLNh0knq+rvw2sCnKP8ZC7P/AlHsHxPpPQMdx4NPgrt7HZDKx4dwG5u6Zy4nEEwC4a93pF9qPXvV64aR2utuPIoQQQgg7Km5ek0BcQhKIy05iup72UzeSkpXDnF5NeaKJP1w9AZs+hP3fASZAAQ2fhY5vQtW7uwGH0WRk3el1zIudx+nk0wBU0VXh5UYv071ud7Qq7V1/JiGEEELcexXqxhzz5s0jKCgInU5HeHg4O3bsKHL8qlWrqFevHjqdjkaNGvHbb79ZHTeZTEycOBE/Pz8cHR2JjIzk+PHjNq+VlZVFWFgYCoWC2NjY0vpI4i54OGl4uf0DAHy8/hg5BqM59D77ObwWAw2eAkxw4DuY1xLWvAY3Tpf4/ZQKJY8GP8oPT/3A++3ep4ZLDa5nXmfazml0Xd2VlUdWkm3ILp0PJ4QQQohyx+6BeOXKlYwcOZJJkyaxe/dumjRpQlRUFJcvX7Y5fuvWrfTq1Yv+/fuzZ88eunXrRrdu3Thw4IBlzLRp05g9ezYLFixg+/btODs7ExUVRWZmZoHrvfHGG/j7+5fZ5xMl0799MFWcNZy6msb3uy/cPOBdH7p/Ba9uhjqPgskIsctgTnP4eQQkXSj0mrfjoHTgyVpP8tPTP/F2xNv4OvtyOeMy721/j8d/eJzvj39PtlGCsRBCCHG/sXvLRHh4OC1btmTu3LkAGI1GAgICGDp0KGPHji0wvkePHqSlpfHLL79Y9rVu3ZqwsDAWLFiAyWTC39+fUaNGMXr0aACSkpLw8fFhyZIl9OzZ03Le77//zsiRI1m9ejWhoaHs2bOHsLCwYtUtLRNlb9HmU7z/22GqeziyYXQHtA6qgoPO/2telu3URvNrlRZa9IN2I8HV567eX2/Qs/r4ahbtW8SVDPO6xTVdazKwyUC6BndFpbRRjxBCCCHKjQrRMqHX69m1axeRkZGWfUqlksjISGJiYmyeExMTYzUeICoqyjI+Li6O+Ph4qzHu7u6Eh4dbXTMhIYFXXnmFr7/+Gien2395Kisri+TkZKtNlK3/RATi46blQmIG474/wOWUgjP81GgBvddA39+gZhswZMH2BfBJE1g/EdKulfj9NSoNver14rdnfmN0i9FU0VXhbMpZxv0zjmd+eoY/Tv+B0WQs+QcUQgghRLlg10B89epVDAYDPj7WM3k+Pj7Ex9u+xW58fHyR4/MeixpjMpno27cvAwcOpEWLFsWqdcqUKbi7u1u2gICAYp0nSk6nVjH6kboArN59ngenbeSD3w5zLTWr4OCgttDvN/jPD1C9BeRkwJZP4JPGsOF9yEgseR0OOvqE9uH3Z35neLPhuGncOJV0itGbRvP8z8+z4ewG5LupQgghRMVl9x5ie5gzZw4pKSm8+eabxT7nzTffJCkpybKdO3euDCsUeZ5vEcA3/cNpWtODzGwjCzefov20jUxde4QbaXrrwQoF1HoYXv4TXvgWfBuDPhU2TzMH483TISulxLU4qZ14udHLrH12La81eQ0XtQvHbhxj+Mbh9Pq1F/9c+EeCsRBCCFEB2TUQV61aFZVKRUJCgtX+hIQEfH19bZ7j6+tb5Pi8x6LGbNiwgZiYGLRaLQ4ODtSubV62q0WLFvTp08fm+2q1Wtzc3Kw2cW+0C6nK94PasLhfSxrXcCddb2D+XydpP20jM9cdJSn9li+6KRRQJwoGbILuX0O1+pCZZO41/qQJbJkN+vQS1+OqcWVQ2CDWPruWlxu9jKODIwevHWTQn4Po/XtvdlwqepUUIYQQQpQvdg3EGo2G5s2bEx0dbdlnNBqJjo4mIiLC5jkRERFW4wHWr19vGR8cHIyvr6/VmOTkZLZv324ZM3v2bPbu3UtsbCyxsbGWZdtWrlzJ+++/X6qfUZQOhULBQ3W9+XFwWz7v3YIGfm6kZuUwe8MJ2k3bwCd/Hic585ZgrFRCgydh0BZ45nOoUgvSr8H6CTA7DLZ/Bjk22i+KyV3rzvBmw/n9md/p06APWpWW2Cux9F/Xn/5/9GfP5T1396GFEEIIcU/YfZWJlStX0qdPHz777DNatWrFrFmz+Pbbbzly5Ag+Pj707t2b6tWrM2XKFMC87FqHDh348MMPeeyxx1ixYgUffPABu3fvpmHDhgBMnTqVDz/8kKVLlxIcHMyECRPYt28fhw4dQqfTFajh9OnTBAcHyyoTFYjRaGLdoXg+Xn+cownmNgh3RzUDHnyAPm2CcNE6FDzJkAP7VsCmqZB41rzPrTo8OAaavgQq9V3VdDn9Mp/v/5zvjn1nWZ6tbfW2DAkbQsOqDe/q2kIIIYS4cxXqTnVz585l+vTpxMfHExYWxuzZswkPDwegY8eOBAUFsWTJEsv4VatWMX78eE6fPk1ISAjTpk2ja9euluMmk4lJkyaxcOFCEhMTadeuHZ9++il16tSx+f4SiCsuo9HEbwcuMevP45y4nApAFWcNrz74AP+JCMRJYyMY5+hhz9ew+SNIuWje5xkEHf4LjbqDysY5d+BS6iU+2/cZP574kRxTDgAdAzoyJGwIdavUvatrCyGEEKL4KlQgrogkEJcvBqOJX/ZdZNafx4m7mgZAVRcNAzvU4qXWgejUNtYMzs6EXYvh75mQlnsjGK8Q6DgWQp8xt1zchXMp51iwdwG/nPrFsjzbI4GPMDhsMA94PHBX1xZCCCHE7UkgLmMSiMunHIORNbEXmR19nLPXzV+c83bV8lrHWvRsVdN2MNanwY5FsGUWZNww7/MOhYfehHqPm7+kdxdOJZ1iQewC1p5eiwkTSoWSrsFdGdRkEDXdat7VtYUQQghROAnEZUwCcfmWbTDy/e7zzI4+wYXEDAD83HUMfqg23VsEoHGwMfubmWy+qcfWOZCVe+MVvzB46C0I6XzXwfjYjWN8Gvsp0WfNX/hUKVQ8VfspXm38Kv4ucvtwIYQQorRJIC5jEogrBn2OkVW7zjF3wwkuJZnvdFfdw5GhD9fm2eY1UKtsBOOMG7B1LmybD9nm9gtqtIKHx8MDHe66poPXDjJvzzz+vvA3AA5KB54NeZZXGr2Cj/Pd3W5aCCGEEDdJIC5jEogrlsxsAyt3nmPexhNcTjEvtVazihPDOoXQLcwfB1vBOO2quY1ixyLIyb1tdFB784xxoO1lAe9E7OVY5sbOZful7QBolBq61+1O/0b9qepY9a6vL4QQQlR2EojLmATiiikz28Cy7WeZ/9cJrqaa73QXXNWZ4Z1CeKKJPyqljbaIlHjzF+92LQZD7t3xanWCh9+C6s3vuqad8TuZu2cuuy/vBsDRwZFe9XrRL7QfHjqPu76+EEIIUVlJIC5jEogrtnR9Dl/HnGHBppPcyL3TXW1vF0ZEhtC1oR9KW8E48Rz8/RHs+QaM5uXUqNsVHhoHvo3uqh6TyUTMxRjmxs5l/9X9ADirnXm+zvOEVQsjxDOE6i7VUSltfClQCCGEEDZJIC5jEojvD6lZOSzdepqFm0+RlGEOxnV9XHm9cwiPNPC1HYyvn4JN02DfSshdTo0G3czBuNrdrTNsMpnYfH4zc2PncuT6EatjOpWOWh61CPEMIcQjhNqetanjWQcvnReKu/zCnxBCCHE/kkBcxiQQ31+SM7NZsuU0i/4+RUqmefa3gZ8br3euQ2R9b9uB88ox+GsKHPze/FqhhEbPm2/w4VXrruoxmoxsOLuBjec2cvzGcU4lnSLLYPs2055aT0I8Q6jtUdsclnOfO6ud76oGIYQQoqKTQFzGJBDfn5LSs/nin1N8ueU0qVnmYNy4hjuvd65DxzrVbAfj+APmYHzkF/NrhQqavmi+JbRH6awzbDAaOJdyjuOJxzl+4zgnEk9w/MZxzqactdz041bVXaoT4hFiFZaD3INQK+/uFtVCCCFERSGBuIxJIL6/3UjTs/DvUyzdepp0vQGApjU9GNm5Du1qV7UdjC/sho0fwIn15tdKNTTvC+1HgZtfmdSZmZPJyaSTnLhhDsh5gflKxhWb4x2UDgS7B1Pbw9xukReY/Zz9pO1CCCHEfUcCcRmTQFw5XE3NYuHmU3wVc5rMbPNMbMsgT17vXIc2tQpZGu3sdtj4HsRtNr920EHLl6HtCHCpdk/qTsxMtITj44nHzYE58Thpeesq38JZ7WyZRc4flmWVCyGEEBWZBOIyJoG4crmcksmCv07xzfYz6HPMwTjiAS9GPlKHlkFVbJ8Utxk2vA/ntplfq50h/FVoMxScCjmnDJlMJi6lXbKaST6eeJy4pDhy8lbNuEU1x2pWvckhniE84P4Ajg6O97h6IYQQ4s5JIC5jEogrp/ikTD796wQrdpxDbzAH4/YhVXm9cx2a1fQseILJBCeizTPGF/eY92ndIGIwtB4EOvd7WL1t2cZsziSduRmSc4PyhdQLNscrUFDTrWaB/uSarjVlWTghhBDligTiMiaBuHK7kJjBvI0n+HbnOXKM5j9CD9Wtxuud69C4hkfBE0wmOPqbucc44YB5n84D2g43zxpryt+KEGnZaZYv7+U9Hr9xnBtZN2yO16q0POD+gGVZuLyw7O1UyCodQgghRBmTQFzGJBALgHPX05m74QTf7T6PITcYR9b34fXOIYT625j9NRrh0BrzqhRXj5n3OVeDdq9Di/8DdfluRTCZTFzLvGY1k3z8xnFOJp4k05Bp8xw3jZtVSM4Lyq4a13tcvRBCiMpGAnEZk0As8jt9NY3ZG46zZs8FcnMxj4b6MqJzCPV8bfz3YTTA/lXw14dwI868z9XPvCJFs97goL13xZcCg9HAhdQLHL9xnGOJxyxf4juTfKbQZeH8nP2s+5M9Qgh2D0aj0tzj6oUQQtyvJBCXMQnEwpaTV1KZHX2cn/ZeJO9P1mON/Xg9MoTa3jZmRA3ZELvcfOe75PPmfe41ocMYaNILVBV7zeAsQxZxSXGWGeVjicc4fuM4l9Mv2xzvoHAg0C2QEM8QAlwD8Hbyttq8dF7SpyyEEKLYJBCXMQnEoijHE1KY9edxft1/CQCFAp5q4s+wTiE8UM2l4Ak5WbD7K9j8EaTGm/dVecC8VFtQO/AMBqXy3n2AMpaUlWSzPzklO6XI81QKFV6OXng73gzJPs4+eDt5U82xGj5O5ucuGhs/YyGEEJWOBOIyJoFYFMfhS8nM+vMYfxxMAECpgGea1WDYwyHU9HIqeEJ2Buz8Av6ZCenXbu7XuIJvI/BrcnOrWgdUDvfok5Q9k8lEQnqCpTf5UuolLqdftmxXM68W2n5xKycHpwKzy/k3HycfvBy95K59Qghxn5NAXMYkEIs7ceBCErP+PMafh82tAiqlgueb12DIw7Wp4WkjGGelwo6F5ttBJxyEHBtfWHPQgU+odUj2blDh+o+Ly2A0cC3zGpfTL5OQnsDl9MtcSb9ieZ73+nazzHkUKKiiq2IJyN5O3lRzujnLnLe5adxklQwhhKigJBCXMQnEoiRizyXy8fpjbDpmvrWyWqWge4sAhjxcGz/3QlaYMOSYV6SI3weX9uZu+0BvI/gpHaBafeuQ7NuwXC7rVlbSs9NvzixnXLaaZc4fnHNMtm9GciudSkc1p2rmgJyvVcPb2Rykqzmaj8mXAYUQovyRQFzGJBCLu7HrzHU+Xn+cf05cBUCjUvJCeE0GdayFj5vu9hcwGs2rU1gCcu6Wcd3GYAVUDbklJDcCRxs3EqkkjCYj1zOvcyX9ivWMc4b1jHNSVlKxr+mp9Sx0ljlv89R6ymyzEELcQxKIy5gEYlEatp+6xsz1x9geZw6yWgclL7UOZGCHWlRzvcPWB5MJks6bg3H+2eSUS7bHewTmC8lh4NcYXLzv7gPdZ7IMWTZnmG/d9EZ9sa6nVqotXwDM38+cNwOd91xujS2EEKVDAnEZk0AsSovJZCLm5DVmrD/GrjPmu8Dp1Er6RAQx4MEH8HK5y57glIRb2i32QuIZ22Nd/W6ZSW4M7jXMy2QIm0wmE0lZSSSkJ3Al40qBHue819czbc3e26ZT6XDTuuGudcdd446H1gN3rbt5X77Xlk1jftQ5FONfF4QQohKRQFzGJBCL0mYymfj7+FVmrD/G3nOJADgoFdTzc6VJDQ+aBHgQFuBBrWouqJR3GVAzbpj7kPMH5avHARv/c+BYxTok+zW575aBuxeyDdlczbhq1ZJhq8c5IyejxO+hVWkLhGQPrYclSOe9dte646Zxs7yWIC2EuF9JIC5jEohFWTGZTGw8epmP1x9n/4WCPazOGhUNq7sTFmAOyU0CPPB31919b2pWKiQcMAflvJB85TAYbXz5TONqbrHIH5K9Qu6rZeDswWQykZadRpI+iaSsJBKzEknOSrY8z9ufnJVc4HVxvyRoi1alxV1jnoG2mn3WuNucic7bpLVDCFHeSSAuYxKIRVkzmUxcTMpk77lE9p5PZO+5RPafTyJNbygwtqqLxjKL3CTAgyY13PFwKoVVD7Iz4fIh65nk+ANgyCo41kEHPg1vWQau/n27DFx5cmuQttpuDde3vC6tIJ1/9rlAuL4lSOtUpfAXOCGEKIYKFYjnzZvH9OnTiY+Pp0mTJsyZM4dWrVoVOn7VqlVMmDCB06dPExISwtSpU+natavluMlkYtKkSSxatIjExETatm3L/PnzCQkJsYx58skniY2N5fLly3h6ehIZGcnUqVPx9/cvVs0SiIU9GIwmTl5JJfZcoiUoH7mUQo6x4B/jIC8nmgR40LiGB2EB7oT6u6NTl8Jtjw3Z5mXg8s8kx+8DfWrBsUo1eNfL98W9Jua1kyvRMnDlmclkIj0n/eYMdG6AtsxA2wjWedvdBGmNUmNz5tlN44az2hkntRPOamfL5uRw83XeMQnVQojiqDCBeOXKlfTu3ZsFCxYQHh7OrFmzWLVqFUePHsXbu+A33rdu3cqDDz7IlClTePzxx1m+fDlTp05l9+7dNGzYEICpU6cyZcoUli5dSnBwMBMmTGD//v0cOnQInc7cK/fxxx8TERGBn58fFy5cYPTo0ZbrF4cEYlFeZGYbOHQp2RyQzyWy93wScVfTCoxzUCqo6+tqmUFuEuBBiLfr3fcjg3kZuOun4FKs9Wxyxo2CYxVKc3tFgWXgPO6+DnFP5A/S+ds5rNo7CgnXdxOk81MpVDeDs4NzgSBtK0S7qF0KPcdBKe0+QtyPKkwgDg8Pp2XLlsydOxcAo9FIQEAAQ4cOZezYsQXG9+jRg7S0NH755RfLvtatWxMWFsaCBQswmUz4+/szatQoS8hNSkrCx8eHJUuW0LNnT5t1/PTTT3Tr1o2srCzU6tvfzlUCsSjPktKz2XfBHJBjzyURey6Rq6kF2xyc8vcj1/CgSYA71T0cS2fmzWSCpHM3bySSF5JT422P9wwqeGtqV3/pS76PmEwmMnIyrAJz/lnnFH0KadlppOWkmR9zt/Ts9JvPc9LLpDatSlsgSFvNUucL0gVmsNVOloDtrHbG0aGU/gwJIe5acfOaXf+fRq/Xs2vXLt58803LPqVSSWRkJDExMTbPiYmJYeTIkVb7oqKiWLNmDQBxcXHEx8cTGRlpOe7u7k54eDgxMTE2A/H169dZtmwZbdq0KTQMZ2VlkZV1M1AkJycX+3MKca+5O6lpH1KN9iHVAHMQuZTbjxx7PpF955LYdz6RNL2BHXHX2RF3c0kwL2dN7iyyOSA3qeGBp3MJ+pEVCvCoad7qP3Fzf0p87goX+ZeBOws3Tpu3Qz/mu4YK3KrfvI5HgPnRPe+xBqhu/xdYUT4oFAqc1E44qZ3wdylee9qtjCYjGTkZNsNyanaq+XVOwWNpObe8zt2yjdmAec3pLEMW1yn+8niFUSqUODk4FT2DnRuiXTQuODk4oXPQoVVp0aq0aFSaAs81Kg06lXmMg9JBArcQpcyugfjq1asYDAZ8fHys9vv4+HDkyBGb58THx9scHx8fbzmet6+wMXn++9//MnfuXNLT02ndurXVrPOtpkyZwjvvvFO8DyZEOaNQKPD3cMTfw5EujfwAcz/yqbx+5POJ7D2XxJH4ZK6l6dlw5DIbjly2nF+zipOl1SIswINQf3ccNSXsR3b1NW91Hrm5L/16bqtFvpnkG6fBmA1JZ82braWTFUrz2sn5Q7IlOAeaA7N8qe++olQoLaGyNGQbsq1mpW8Nzek56aTqUwsEasvzW84zYcJoMpKanUpqto2++lKgQGEzLBcWpPP2FRm2cwO51XnK3PMcbo7XKDWolKXwXQQhyplK/W+RY8aMoX///pw5c4Z33nmH3r1788svv9j8m/ebb75pNTOdnJxMQEDAvSxXiFKlUioI8XElxMeV51uY/1vOzDZwOK8f+XwSe88lcupqGmevp3P2ejo/771oObeuj6tlBtncj+yCg6qEaxM7VYEHOpq3PEYjpCaYZ48Tc0Nx3vPEc+Z2jJxMSL5g3rD9r0q4+FrPLnvUBPd8wVktS4dVZmqVGg+VBx543PW18lpCipqVvnUGOzU7lbTsNPQGvWWWOsuQZfU677nlfTCRacgk05B51zWXhIPSwSpU5w/jhQXxwo5pVBoclA44KB1QK9SW55Z9Sut9RY1RKVQycy5KzK6BuGrVqqhUKhISEqz2JyQk4Ovra/McX1/fIsfnPSYkJODn52c1JiwsrMD7V61alTp16lC/fn0CAgLYtm0bERERBd5Xq9Wi1cpMk7i/6dQqmtb0pGlNT8u+vH7kfefNvcix5xK5kpLFoUvJHLqUzP92nAPAUa2iUXV3mgS4565s4UENz7vopVQqwc3PvNUML3jcZIK0K7kB+Yw5JFvCc+7z7HRzz3JqPJzfYft9nKvZmGGueXOf1qVk9YtKJ39LSDWqleq1TSYT2cZsMg2ZNsOyZV9OwVCtN+YL2zmFnJf7PP/18z/m/zJkjjGHHGMOadkFv7xrb5aArCg6WDsoHXBQWO8vbEz+YzbH3DKu0DH53lOtVKNWqa3+kuCgkFYYe7JrINZoNDRv3pzo6Gi6desGmL9UFx0dzZAhQ2yeExERQXR0NCNGjLDsW79+vSXEBgcH4+vrS3R0tCUAJycns337dgYNGlRoLUajEcCqT1gIYbsfOT4502oWed/5JFKzcthx+jo7Tt/swazirLGsaJHXl1ylJP3ItigU4OJt3mq0KHjcZDK3YiSesQ7JeTPMiWdBn2IO1WlX4MIu2+/jWOWWNoxbWjN07qXzeYQogkKhMLcsqErpz88dyjHmFB3EbQTqwsbdeiwvYFs2Uw7ZhmxyTObX2cbsAmOyjdmYbNxZM+94RaRUKK3aVQqbUdeqtKhV6tvOvN/a+pL3309h56iV6kodyO2+ysTKlSvp06cPn332Ga1atWLWrFl8++23HDlyBB8fH3r37k316tWZMmUKYF4WrUOHDnz44Yc89thjrFixgg8++KDAsmsffvih1bJr+/btsyy7tn37dnbu3Em7du3w9PTk5MmTTJgwgYSEBA4ePFismWBZZUKIm4xGE6euphJ7LsmyPvLhS8lkGwr+z0tAFUea5M4gNwnwINTfDSeNHf5ubjJBZmLBkJy/PSOz4J0CC9C552vBuLU1IwAcPc3hXQhRqgxGgyU0FxWc80L2bccUd9wdXuvW/XnPs43Zli91lhdFBWuNsvAwXeQ+ZcEg7qnzxNfZdidAaasQq0yAeRm1K1euMHHiROLj4wkLC2Pt2rWWL8WdPXsWpfJmX2KbNm1Yvnw548ePZ9y4cYSEhLBmzRpLGAZ44403SEtLY8CAASQmJtKuXTvWrl1rWYPYycmJ77//nkmTJpGWloafnx+PPvoo48ePl7YIIUpAqVRQ29uV2t6uPNe8BgBZOQYOX0qxrI8cez6RU1fSOHc9g3PXM/hl3yUgt5fZ2+XmrahreFDH5y76kYtLoTCHVUdP8zJvtmQm3QzKVjPMuVvGdfOYzP2QsN/2NTSuBUNy/hlnJy8JzEKUgEqpQoUKrari/v+20WS02aKS1+ZSnFn5W/dlG7LNz40FjxfVmw43V1tJIaVMP3eX4C5Me3Bamb7HnbL7DHFFJTPEQty5pIxs9p9PstyKOvZcIpdTCrYp6dRKanu7EFjFmUAvJwK9nKiZ+9zXTYeyNG4mUhqyUgsG5fyv067c/hpqJ+uQ7B5gXoXDxRucvcHFxxyaZT1mIUQpy+tNLxCmbYVoGwG7QJAvRgDXG/REBkbyZvibty+wFFSYG3NUVBKIhSgd8UmZlqXf9uWukZySVXgPoMZBSc0qTgRWcaKml/kxsKozgVWcqOHphMahjGeW74Q+HZLO37JKRr7AXNhNSgpQgHPV3ICcb8sLzC7VzI/O3ubwrCxHPwMhhLAjCcRlTAKxEGXDaDQRdy2NU1fSOHMtjTPX0jlzPZ2z19I4fyODHGPh/5OlVICfu2PurHLu7HJecPZyxkVbzmZZs3OXjcu/SkbSefNyc2lXch+vgo0vDxVKoTKH58ICsyVQ+0h/sxDivieBuIxJIBbi3ssxGLmYmMmZ6+agfPZ6Oqdz10k+cy2djGxDkedXddGYZ5e9nHMfbwZnL2dN+fyGtSEH0q9B2mVzQE69Yh2YUy+bt7TL5nF3QulQvFlnF2/zlwfL489HCCGKIIG4jEkgFqJ8MZlMXEnN4uy1dE5fM88on8kNymeupXEjvehvcztrVNT0cs5twXCy9C/XrOKEv4cjqvLSt1wUQ7Z5RtlWYL51X2binV1bpckNyNWKnnV2rgZaVwnPQohyQQJxGZNALETFkpyZzdlruQH5ehpnrpofz15L51JyJkX9L6FapSDA82bPcl5wDqpq7lvWqSvgrWxzsvIF5LxZ58v5AvTlm6+zku/s2g6OhQRmGzPQmtK5BbMQQtgigbiMSSAW4v6RmW3g/I0MS8+yuQXD/PzcjXSb6ynnUSjA101HzSpOBHk55/Yrm2eYa3o54e6ovoefpIxkZ+QGZBttGre2cehT7+zaaud8Ybka6DzAMXfTeeQujecButxHR09z+4ayAv4lRAhxz0kgLmMSiIWoHAxGE5eSMsyzy9fTOX0tzTLTfPZ6OqlFrIgB4OmkvtmKkduCEZS7KkY1V2357Fu+G/q0ogNz/kCdk1Hy99G6g6P7LaG5qOe5r7Vu0s4hRCUigbiMSSAWQphMJq6n6c09y3lf9MsNzmeupXE1VV/k+Y5qFTVzV8EI8jK3YtSs4oSfuw4fNx1uOof7LzDnMZnMs8n5e5zTr0JGorm/OSMRMm6Yb3xieZ545zPQt1IozTPMjp7WQbk4z9VOEqaFqGAkEJcxCcRCiNtJzcrhbG5YPm2ZVTYH54uJGRSxghxgDsw+blq83XT4uunwcdPi42YOy77uOnxcdXi7aStmD3NJGbJthObEfPtuFP48J/Pu3lupvrMAnf+5Q8W9m5oQFZkE4jImgVgIcTf0OUbO38hbY/lmWD57PZ2E5CySMopeFSM/Dyc1vm663OBsDs35Q7Svmw4vF23FWCmjLGVn3j4023yeCMbi/z5scnAsOjRrXUHjYv6SodbV/Khxzt3nAloXmaEWogQkEJcxCcRCiLKUoTdwOSWT+KRMElKySEjKJCE5k/jkTC4nZxGfbH6dlWMs1vVUSgXVXLQFZpm9XbXm2Wa3StCmUVImk7k32tas9O2eZyaBqXi/o9tT5AvJzuaQnPf8dmFa4wyafMfyxsnMtbjPSSAuYxKIhRD2ZjKZSMrIJiFfQL6cG5oTkrNIyN13JSXrtu0ZeXRqpSUc++Sbcb75uhK2adwNo9G8bN3tArQ+FbJSzcFbn5q7peXuS+WO7lZ4J5Tqm+FZ62IdpC2hO1+YLk4IlxVARDkigbiMSSAWQlQUOQYj19L05tnm5Lwtq8CM8522afi46vBx1+GTO8ssbRplxGSC7PSbYbmo4FzgdVq+fSm5r9PuboWP23FwLCJc5w/UuccdtDc3lRYcdOCgMT+qch8tY/LtU5WzW7GLckkCcRmTQCyEuN9kZhssYTk+b7Y5f8tGbgtHcds0lAqo5qrN199s+4uBbo7SpnHPGXIgOy1fuM4XlguE63zH8vZbzss9NysVTEXfOr3UKZS3hOa8oJwvPBcI1oUEbZvnaG2EdQnmFU1x85r8FoUQQgCgU6sI9HIm0Kvwu8eZTCaSM3Is4Tj/jHN8vpaNvDYN80x0FpBUxPua2zS8XbV4Ommo4qzB01mDp5Pa6nUVJ/Oj9DmXApUDqNzNS9CVBpPJfPfDAuE6xXqW2ipM5x7L0YMhy3x+TpZ5NRCD3vyYk/eYZR5jzLfut8lonjnPTi+dz1BSClUhobk4M95aCg3xBYK6jTCe/32USvv+HCo4mSEuIZkhFkKIwhmMJq6mZt12xjkx/c5Xb3BQKvBw0lDFWX3bAJ333FmjkhB9PzAaCg/NhnzhudAx+YN3VsHXtq5j6xxj0TfksQuVppAAXUiILpUwfksoL4ez5TJDLIQQwm5USoWlNaIomdkGSw/z1dQsrqfpSUzXcz0tmxvpeq6n6W8+pulJ0xvIyQ3bV1Ozil2PRqXE09nGjLOT2vzcWVPgmKNGvhxW7ihVoHEyb/ZkM5jfGrRthfVbZrxt7itsf2bBY/m/bGnQmzd9it1+LObZ8mKE8eAO0HaY/eq0QQKxEEIIu9GpVdT0Mt+trzgysw0kpmdbB+V0PTcKCdDX0vRk5RjRG4z52jeKW5vS0qaRF5htBujcRw8ntay+UVmUh2BuMplvVGMVoouY6bbsLyp027jO7YK6VRuLwdybnp1WdO1OVcv2Z1MCEoiFEEJUGDq1Cl93Fb7uRc8855ehN3A93RyQbwZoPdfTs3Mfbz2Wjd5gJDPbyMWkTC4mFf8Od84ale0AfUuwNs9Em2es1Srp/RQloFDktjhozEvf2Yshp4ge8ELCuEdN+9VbCAnEQggh7muOGhXVNY5U93As1niTyUSa3mAJyXmB+UYhATqvvcNgNJ+Xps/g/I3iL2vmqnPATafGzVGd+9z82lXngKtOjZtj7qNln0O+sTIrLexM5WDeNIV/GbcikEAshBBC5KNQKHDROuCidSCgSvH+SdxkMpGcmWMVmAsP0LnH0vWYTJCSmUNKZg4XEku2NrBGpbQKya75ArX5UW3zuFtu2HbROuAgs9SikpNALIQQQtwlhUKBu6Mad0c1QRRvpsxgNJGckc31dD3JGdmkZOaQnJmdG5CzSc7Ifcy8+Zg3LiUzm5SsHPNdpXNvvHItTV/i+p00Khsz0Grccmep8/YVNnstq3iIik4CsRBCCGEHKqXCvFycs6ZE5xuNJtL0OZbAnHJLYE7ODdh5wdpW4M7INt9MI11vIF1vID65ZJ9FqeBmcL4lMLvdsv/mTLX50UXrgJNGhZPGQe5sKOxGArEQQghRASmVitxQqQaK1x99q2yD8eaMc26gzj8jnVJUoM59zDaYMJogKSM79/bfJb8ttKNahbPWHI6dNCpzWNY64KxR4Zz7aP3aASetCmeNA865wdo533FHtQqlhGxRDBKIhRBCiEpKrVJSJXf1i5IwmUxk5RgtQdpWy0f+IH1ra0hyZjbpegMGo3k93YxsQ+6sdcnbP26VPyQ7aRxw1uYL03nHckN4XuB2sQrX+Y5rVTiqpT3kfiSBWAghhBAlolAo0KlV6NQqvEt409a8UJ2uN5CWlUOaPoe0LAPpuY9pWTnm53nH845ZXudYnZueZSBNn0Nuxra0g1wptc9M0WE636y1kzZ3lluTb1Y73wx43s9Op1aiUSklaNuRBGIhhBBC2E3+UF3SmepbmUwmMrONVgHZHJgNpGflkJoXonOPp+YL3em5oTtNbx6TmpVj3qc35F4bUnOvQUrxb/RyO0qFuWUkf0h21KjQOahw1KjQ5j7qHHL3545zzBt767l5x3OvodMoLeNl7euCykUgnjdvHtOnTyc+Pp4mTZowZ84cWrVqVej4VatWMWHCBE6fPk1ISAhTp06la9euluMmk4lJkyaxaNEiEhMTadu2LfPnzyckJASA06dPM3nyZDZs2EB8fDz+/v689NJLvPXWW2g0pfOHUQghhBD2oVAocNSYwyAupXNNo9FEZk5eQDZYZrJvDd15s9WWMJ1vpjtNnzfznUOG3twekjeTbTSRu461oXQKLoJKqcgXoPOFZ7UK7S2vdWolunzB3LLPaoyN/bnhvaIs6Wf3QLxy5UpGjhzJggULCA8PZ9asWURFRXH06FG8vb0LjN+6dSu9evViypQpPP744yxfvpxu3bqxe/duGjZsCMC0adOYPXs2S5cuJTg4mAkTJhAVFcWhQ4fQ6XQcOXIEo9HIZ599Ru3atTlw4ACvvPIKaWlpfPTRR/f6RyCEEEKIck6pVOS2OjhAKd0YzmQykW0wkZFtICu3fzoz25j7mPtabyAzx0CG3mjZZ2tsZqHnGsnIvYYpN3wbjKabs9xlTK1S5M5Q3wzNner78N9H65X5e98JhcmU9+Oxj/DwcFq2bMncuXMBMBqNBAQEMHToUMaOHVtgfI8ePUhLS+OXX36x7GvdujVhYWEsWLAAk8mEv78/o0aNYvTo0QAkJSXh4+PDkiVL6Nmzp806pk+fzvz58zl16lSx6k5OTsbd3Z2kpCTc3ErYOCWEEEIIcQ/k9Wpn3RqaLSHben+G3kBWXpjOF8AzC5xrzBfQb44ryrPNajCje5N78rmLm9fsOkOs1+vZtWsXb775pmWfUqkkMjKSmJgYm+fExMQwcuRIq31RUVGsWbMGgLi4OOLj44mMjLQcd3d3Jzw8nJiYmEIDcVJSElWqVCm01qysLLKybvYKJSeXcLFGIYQQQoh7LH+vtjvqMn2vvPCdYZndvjl7nZVtoIpL+WtPtWsgvnr1KgaDAR8fH6v9Pj4+HDlyxOY58fHxNsfHx8dbjuftK2zMrU6cOMGcOXOKbJeYMmUK77zzTtEfSAghhBCikssfviuKitHpXIYuXLjAo48+yvPPP88rr7xS6Lg333yTpKQky3bu3Ll7WKUQQgghhCgrdg3EVatWRaVSkZCQYLU/ISEBX19fm+f4+voWOT7vsTjXvHjxIg899BBt2rRh4cKFRdaq1Wpxc3Oz2oQQQgghRMVn10Cs0Who3rw50dHRln1Go5Ho6GgiIiJsnhMREWE1HmD9+vWW8cHBwfj6+lqNSU5OZvv27VbXvHDhAh07dqR58+YsXrwYpbLST5YLIYQQQlRKdl92beTIkfTp04cWLVrQqlUrZs2aRVpaGv369QOgd+/eVK9enSlTpgAwfPhwOnTowIwZM3jsscdYsWIF//77r2WGV6FQMGLECN577z1CQkIsy675+/vTrVs34GYYDgwM5KOPPuLKlZv3rylsZloIIYQQQtyf7B6Ie/TowZUrV5g4cSLx8fGEhYWxdu1ay5fizp49azV726ZNG5YvX8748eMZN24cISEhrFmzxrIGMcAbb7xBWloaAwYMIDExkXbt2rF27Vp0Oh1gnlE+ceIEJ06coEaNGlb12HkVOiGEEEIIcY/ZfR3iikrWIRZCCCGEKN+Km9ekcVYIIYQQQlRqdm+ZqKjyJtblBh1CCCGEEOVTXk67XUOEBOISSklJASAgIMDOlQghhBBCiKKkpKTg7u5e6HHpIS4ho9HIxYsXcXV1RaFQlPn7JScnExAQwLlz56RnuZKQ33nlI7/zykl+75WP/M7vHZPJREpKCv7+/kUusSszxCWkVCoLrFBxL8hNQSof+Z1XPvI7r5zk9175yO/83ihqZjiPfKlOCCGEEEJUahKIhRBCCCFEpSaBuILQarVMmjQJrVZr71LEPSK/88pHfueVk/zeKx/5nZc/8qU6IYQQQghRqckMsRBCCCGEqNQkEAshhBBCiEpNArEQQgghhKjUJBALIYQQQohKTQJxBTBv3jyCgoLQ6XSEh4ezY8cOe5ckytCUKVNo2bIlrq6ueHt7061bN44ePWrvssQ99OGHH6JQKBgxYoS9SxFl6MKFC7z00kt4eXnh6OhIo0aN+Pfff+1dlihDBoOBCRMmEBwcjKOjI7Vq1WLy5MnI+gb2J4G4nFu5ciUjR45k0qRJ7N69myZNmhAVFcXly5ftXZooI5s2bWLw4MFs27aN9evXk52dzSOPPEJaWpq9SxP3wM6dO/nss89o3LixvUsRZejGjRu0bdsWtVrN77//zqFDh5gxYwaenp72Lk2UoalTpzJ//nzmzp3L4cOHmTp1KtOmTWPOnDn2Lq3Sk2XXyrnw8HBatmzJ3LlzATAajQQEBDB06FDGjh1r5+rEvXDlyhW8vb3ZtGkTDz74oL3LEWUoNTWVZs2a8emnn/Lee+8RFhbGrFmz7F2WKANjx45ly5Yt/P333/YuRdxDjz/+OD4+PnzxxReWfc8++yyOjo588803dqxMyAxxOabX69m1axeRkZGWfUqlksjISGJiYuxYmbiXkpKSAKhSpYqdKxFlbfDgwTz22GNWf+bF/emnn36iRYsWPP/883h7e9O0aVMWLVpk77JEGWvTpg3R0dEcO3YMgL179/LPP//QpUsXO1cmHOxdgCjc1atXMRgM+Pj4WO338fHhyJEjdqpK3EtGo5ERI0bQtm1bGjZsaO9yRBlasWIFu3fvZufOnfYuRdwDp06dYv78+YwcOZJx48axc+dOhg0bhkajoU+fPvYuT5SRsWPHkpycTL169VCpVBgMBt5//31efPFFe5dW6UkgFqIcGzx4MAcOHOCff/6xdymiDJ07d47hw4ezfv16dDqdvcsR94DRaKRFixZ88MEHADRt2pQDBw6wYMECCcT3sW+//ZZly5axfPlyQkNDiY2NZcSIEfj7+8vv3c4kEJdjVatWRaVSkZCQYLU/ISEBX19fO1Ul7pUhQ4bwyy+/sHnzZmrUqGHvckQZ2rVrF5cvX6ZZs2aWfQaDgc2bNzN37lyysrJQqVR2rFCUNj8/Pxo0aGC1r379+qxevdpOFYl7YcyYMYwdO5aePXsC0KhRI86cOcOUKVMkENuZ9BCXYxqNhubNmxMdHW3ZZzQaiY6OJiIiwo6VibJkMpkYMmQIP/zwAxs2bCA4ONjeJYky1qlTJ/bv309sbKxla9GiBS+++CKxsbEShu9Dbdu2LbCc4rFjxwgMDLRTReJeSE9PR6m0jl4qlQqj0WinikQemSEu50aOHEmfPn1o0aIFrVq1YtasWaSlpdGvXz97lybKyODBg1m+fDk//vgjrq6uxMfHA+Du7o6jo6OdqxNlwdXVtUCPuLOzM15eXtI7fp96/fXXadOmDR988AHdu3dnx44dLFy4kIULF9q7NFGGnnjiCd5//31q1qxJaGgoe/bsYebMmfzf//2fvUur9GTZtQpg7ty5TJ8+nfj4eMLCwpg9ezbh4eH2LkuUEYVCYXP/4sWL6du3770tRthNx44dZdm1+9wvv/zCm2++yfHjxwkODmbkyJG88sor9i5LlKGUlBQmTJjADz/8wOXLl/H396dXr15MnDgRjUZj7/IqNQnEQgghhBCiUpMeYiGEEEIIUalJIBZCCCGEEJWaBGIhhBBCCFGpSSAWQgghhBCVmgRiIYQQQghRqUkgFkIIIYQQlZoEYiGEEEIIUalJIBZCCCGEEJWaBGIhhBB3RaFQsGbNGnuXIYQQJSaBWAghKrC+ffuiUCgKbI8++qi9SxNCiArDwd4FCCGEuDuPPvooixcvttqn1WrtVI0QQlQ8MkMshBAVnFarxdfX12rz9PQEzO0M8+fPp0uXLjg6OvLAAw/w3XffWZ2/f/9+Hn74YRwdHfHy8mLAgAGkpqZajfnyyy8JDQ1Fq9Xi5+fHkCFDrI5fvXqVp59+GicnJ0JCQvjpp5/K9kMLIUQpkkAshBD3uQkTJvDss8+yd+9eXnzxRXr27Mnhw4cBSEtLIyoqCk9PT3bu3MmqVav4888/rQLv/PnzGTx4MAMGDGD//v389NNP1K5d2+o93nnnHbp3786+ffvo2rUrL774ItevX7+nn1MIIUpKYTKZTPYuQgghRMn07duXb775Bp1OZ7V/3LhxjBs3DoVCwcCBA5k/f77lWOvWrWnWrBmffvopixYt4r///S/nzp3D2dkZgN9++40nnniCixcv4uPjQ/Xq1enXrx/vvfeezRoUCgXjx49n8uTJgDlku7i48Pvvv0svsxCiQpAeYiGEqOAeeughq8ALUKVKFcvziIgIq2MRERHExsYCcPjwYZo0aWIJwwBt27bFaDRy9OhRFAoFFy9epFOnTkXW0LhxY8tzZ2dn3NzcuHz5ckk/khBC3FMSiIUQooJzdnYu0MJQWhwdHYs1Tq1WW71WKBQYjcayKEkIIUqd9BALIcR9btu2bQVe169fH4D69euzd+9e0tLSLMe3bNmCUqmkbt26uLq6EhQURHR09D2tWQgh7iWZIRZCiAouKyuL+Ph4q30ODg5UrVoVgFWrVtGiRQvatWvHsmXL2LFjB1988QUAL774IpMmTaJPnz68/fbbXLlyhaFDh/Kf//wHHx8fAN5++20GDhyIt7c3Xbp0ISUlhS1btjB06NB7+0GFEKKMSCAWQogKbu3atfj5+Vntq1u3LkeOHAHMK0CsWLGC1157DT8/P/73v//RoEEDAJycnPjjjz8YPnw4LVu2xMnJiWeffZaZM2dartWnTx8yMzP5+OOPGT16NFWrVuW55567dx9QCCHKmKwyIYQQ9zGFQsEPP/xAt27d7F2KEEKUW9JDLIQQQgghKjUJxEIIIYQQolKTHmIhhLiPSVecEELcnswQCyGEEEKISk0CsRBCCCGEqNQkEAshhBBCiEpNArEQQgghhKjUJBALIYQQQohKTQKxEEIIIYSo1CQQCyGEEEKISk0CsRBCCCGEqNT+Hwa0SIjBm8edAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['drop=0','drop=0.5','drop=0.8','Train Loss']\n",
    "train_loss=[trainloss11,trainloss311,trainloss321]\n",
    "draw(name,train_loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "06ae1848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['drop=0','drop=0.5','drop=0.8','Test Loss']\n",
    "test_loss=[testloss11,testloss311,testloss321]\n",
    "draw(name,test_loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d8e3be",
   "metadata": {},
   "source": [
    "## 用torch.nn实现dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "46bef251",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NetNN3(nn.Module):\n",
    "    def __init__(self,drop=0):\n",
    "        super(NetNN3,self).__init__()\n",
    "        num_inputs,num_hiddens,num_outputs=784,256,10\n",
    "        self.input_layer=nn.Flatten()\n",
    "        self.hidden_layer=nn.Linear(num_inputs,num_hiddens)\n",
    "        self.output_layer=nn.Linear(num_hiddens,num_outputs)\n",
    "        self.drop=nn.Dropout(drop)\n",
    "        self.relu=nn.ReLU()\n",
    "    def forward(self,x):\n",
    "        x=self.drop(self.input_layer(x))\n",
    "        x=self.relu(self.drop(self.hidden_layer(x)))\n",
    "        x=self.output_layer(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ea15136a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0062 | test loss:0.0045 | train acc: 0.4772 | test acc: 0.6058\n",
      "epoch: 2 | train loss:0.0039 | test loss:0.0037 | train acc: 0.6478 | test acc: 0.6625\n",
      "epoch: 3 | train loss:0.0034 | test loss:0.0034 | train acc: 0.6928 | test acc: 0.6941\n",
      "epoch: 4 | train loss:0.0031 | test loss:0.0031 | train acc: 0.7193 | test acc: 0.7218\n",
      "epoch: 5 | train loss:0.0029 | test loss:0.0030 | train acc: 0.7352 | test acc: 0.7324\n",
      "epoch: 6 | train loss:0.0028 | test loss:0.0029 | train acc: 0.7457 | test acc: 0.7439\n",
      "epoch: 7 | train loss:0.0027 | test loss:0.0028 | train acc: 0.7543 | test acc: 0.7470\n",
      "epoch: 8 | train loss:0.0027 | test loss:0.0027 | train acc: 0.7618 | test acc: 0.7516\n",
      "epoch: 9 | train loss:0.0026 | test loss:0.0027 | train acc: 0.7656 | test acc: 0.7578\n",
      "epoch: 10 | train loss:0.0025 | test loss:0.0027 | train acc: 0.7704 | test acc: 0.7588\n",
      "10轮 总用时: 122.82s\n"
     ]
    }
   ],
   "source": [
    "net312=NetNN3(0.5)\n",
    "momentum=0.5\n",
    "momentum_optimizer=torch.optim.SGD(net312.parameters(),lr=0.01,momentum=0.5)\n",
    "trainloss312,testloss312,trainacc312,testacc312=trainNN(net312,traindataloader,testdataloader,torch.nn.CrossEntropyLoss(),10,0.01,momentum_optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "3162433d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.0075 | test loss:0.0061 | train acc: 0.2989 | test acc: 0.4298\n",
      "epoch: 2 | train loss:0.0053 | test loss:0.0050 | train acc: 0.4843 | test acc: 0.5215\n",
      "epoch: 3 | train loss:0.0047 | test loss:0.0046 | train acc: 0.5487 | test acc: 0.5574\n",
      "epoch: 4 | train loss:0.0043 | test loss:0.0043 | train acc: 0.5834 | test acc: 0.5913\n",
      "epoch: 5 | train loss:0.0041 | test loss:0.0042 | train acc: 0.6039 | test acc: 0.6042\n",
      "epoch: 6 | train loss:0.0040 | test loss:0.0040 | train acc: 0.6197 | test acc: 0.6177\n",
      "epoch: 7 | train loss:0.0039 | test loss:0.0039 | train acc: 0.6307 | test acc: 0.6308\n",
      "epoch: 8 | train loss:0.0038 | test loss:0.0039 | train acc: 0.6412 | test acc: 0.6320\n",
      "epoch: 9 | train loss:0.0037 | test loss:0.0038 | train acc: 0.6450 | test acc: 0.6453\n",
      "epoch: 10 | train loss:0.0036 | test loss:0.0037 | train acc: 0.6559 | test acc: 0.6496\n",
      "10轮 总用时: 122.53s\n"
     ]
    }
   ],
   "source": [
    "net322=NetNN3(0.8)\n",
    "momentum=0.5\n",
    "momentum_optimizer=torch.optim.SGD(net322.parameters(),lr=0.01,momentum=0.5)\n",
    "trainloss322,testloss322,trainacc322,testacc322=trainNN(net322,traindataloader,testdataloader,torch.nn.CrossEntropyLoss(),10,0.01,momentum_optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "608e9258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['drop=0','drop=0.5','drop=0.8','Train Loss']\n",
    "train_loss=[trainloss12,trainloss312,trainloss322]\n",
    "draw(name,train_loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "174f23a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['drop=0','drop=0.5','drop=0.8','Test Loss']\n",
    "test_loss=[testloss12,testloss312,testloss322]\n",
    "draw(name,test_loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b09cb87",
   "metadata": {},
   "source": [
    "## 可以看到加上了dropout以后损失反而大了，不过将训练集损失和测试集损失对比发现加上dropout以后测试集损失下降更快，说明dropout还是起到作用了，实验效果不明显可能是由于迭代次数过少。由于我的电脑训练时间较长，因此不能再增加迭代次数了。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b658f9e",
   "metadata": {},
   "source": [
    "# 对多分类任务实验中实现早停机制，并在测试集上测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "426033ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "index=list(range(len(traindataset)))\n",
    "random.shuffle(index)\n",
    "n=int(0.8*len(traindataset))\n",
    "train_index,val_index=index[:n],index[n:]\n",
    "train_dataset, train_labels = traindataset.data[train_index], traindataset.targets[train_index]\n",
    "val_dataset, val_labels = traindataset.data[val_index], traindataset.targets[val_index]\n",
    "T_dataset = torch.utils.data.TensorDataset(train_dataset,train_labels)\n",
    "V_dataset = torch.utils.data.TensorDataset(val_dataset,val_labels)\n",
    "T_dataloader = torch.utils.data.DataLoader(dataset=T_dataset,batch_size=256,shuffle=True)\n",
    "V_dataloader = torch.utils.data.DataLoader(dataset=V_dataset,batch_size=256,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "70244172",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainNN4(model,train_iter,val_iter,epochs=10,lr=0.01,weight_decay=0.0):\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.01, betas=(0.9,0.999),eps=1e-6)\n",
    "    criterion = torch.nn.CrossEntropyLoss() \n",
    "    train_loss_list=[]\n",
    "    train_acc_list=[]\n",
    "    val_loss_list=[]\n",
    "    val_acc_list=[]\n",
    "    begintime = time.time()\n",
    "    flag_stop = 0\n",
    "    for epoch in range(1000):\n",
    "        train_l, train_epoch_count, val_epoch_count = 0, 0, 0\n",
    "        n1=0\n",
    "        for data, labels in train_iter:\n",
    "            data, labels = data.to(torch.float32).to(device), labels.to(device)\n",
    "            pred = model(data)\n",
    "            train_each_loss = criterion(pred, labels.view(-1))  \n",
    "            optimizer.zero_grad()  \n",
    "            train_each_loss.backward()  \n",
    "            optimizer.step()  \n",
    "            train_l += train_each_loss.item()\n",
    "            train_epoch_count += (pred.argmax(dim=1)==labels).sum()\n",
    "            n1+=labels.shape[0]\n",
    "        train_acc_list.append(train_epoch_count/len(train_dataset))\n",
    "        train_loss_list.append(train_l)  \n",
    "        with torch.no_grad():\n",
    "            val_loss, val_epoch_count= 0, 0\n",
    "            n2=0\n",
    "            for data, labels in V_dataloader:\n",
    "                data, labels = data.to(torch.float32).to(device), labels.to(device)\n",
    "                pred = model(data)\n",
    "                val_each_loss = criterion(pred,labels)\n",
    "                val_loss += val_each_loss.item()\n",
    "                val_epoch_count += (pred.argmax(dim=1)==labels).sum()\n",
    "                n2+=labels.shape[0]\n",
    "            val_loss_list.append(val_loss)\n",
    "            val_acc_list.append(val_epoch_count / len(val_dataset))\n",
    "            # 若连续五次验证集的损失值连续增大，则停止运行，否则继续运行\n",
    "            if epoch > 5 and val_loss_list[-1] > val_loss_list[-2]:\n",
    "                flag_stop += 1\n",
    "                if flag_stop == 5 or epoch > 35:\n",
    "                    print('停止运行，防止过拟合')\n",
    "                    break\n",
    "            else:\n",
    "                flag_stop = 0\n",
    "        if epoch == 0 or (epoch + 1) % 5 == 0:\n",
    "            print('epoch: %d | train loss:%.4f | val loss:%.4f | train acc:%.4f | val acc:%.4f' % (epoch + 1, train_loss_list[-1], val_loss_list[-1],\n",
    "                                                                                                                     train_acc_list[-1],val_acc_list[-1]))\n",
    "    endtime = time.time()\n",
    "    print(\"多分类早停机制实现 %d轮 总用时: %.2fs\" % (epochs, endtime - begintime))\n",
    "    return train_loss_list,val_loss_list,train_acc_list,val_acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c2429b55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:2605.9718 | val loss:87.2720 | train acc:0.2836 | val acc:0.3192\n",
      "epoch: 5 | train loss:369.3767 | val loss:91.4393 | train acc:0.2453 | val acc:0.2573\n",
      "epoch: 10 | train loss:417.6725 | val loss:104.5391 | train acc:0.1446 | val acc:0.1406\n",
      "停止运行，防止过拟合\n",
      "多分类早停机制实现 1000轮 总用时: 17.78s\n"
     ]
    }
   ],
   "source": [
    "net4 = NetNN3(0.5)\n",
    "net4 = net4.to(device)\n",
    "trainL4, valL4, trainAcc4, valAcc4= trainNN4(model=net4,train_iter=T_dataloader,val_iter=V_dataloader,epochs = 1000,lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9e7c8dfe",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=['Train Loss','Val Loss','Loss']\n",
    "loss=[trainL4,valL4]\n",
    "draw(name,loss,'Epoch','Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc44f677",
   "metadata": {},
   "source": [
    "## 可以看到测试集的损失随着epoch增加而略有上升，有可能发生过拟合，所以需要早停机制"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-pytorch] *",
   "language": "python",
   "name": "conda-env-.conda-pytorch-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
